Release: v4.51.0

examples/flax/question-answering/run_qa.py

@@ -61,7 +61,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risk.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recognition/requirements.txt")
 

examples/flax/text-classification/run_flax_glue.py

@@ -56,7 +56,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/token-classification/run_flax_ner.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/audio-classification/run_audio_classification.py

@@ -45,7 +45,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
 

examples/pytorch/contrastive-image-text/run_clip.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/image-pretraining/run_mae.py

@@ -43,7 +43,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim.py

@@ -48,7 +48,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -53,7 +53,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/instance-segmentation/run_instance_segmentation.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt")
 

examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt")
 

examples/pytorch/language-modeling/run_clm.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_fim.py

@@ -58,7 +58,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_fim_no_trainer.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_mlm.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")

examples/pytorch/language-modeling/run_plm.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/multiple-choice/run_swag.py

@@ -46,7 +46,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = logging.getLogger(__name__)
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -54,7 +54,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 # You should update this to your particular problem to have better documentation of `model_type`

examples/pytorch/object-detection/run_object_detection.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/object-detection/requirements.txt")
 

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logging.basicConfig(level=logging.INFO)
 logger = get_logger(__name__)

examples/pytorch/question-answering/run_qa.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/summarization/run_summarization.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

examples/pytorch/text-classification/run_classification.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/text-classification/run_xnli.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")

examples/pytorch/translation/run_translation.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
 

examples/pytorch/translation/run_translation_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")

examples/tensorflow/contrastive-image-text/run_clip.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version(
     "datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/contrastive-image-text/requirements.txt"

examples/tensorflow/image-classification/run_image_classification.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/tensorflow/multiple-choice/run_swag.py

@@ -50,7 +50,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/question-answering/run_qa.py

@@ -62,7 +62,7 @@ except (ModuleNotFoundError, ImportError):
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/summarization/run_summarization.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 # region Checking dependencies
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/tensorflow/text-classification/run_glue.py

@@ -47,7 +47,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 task_to_keys = {
     "cola": ("sentence", None),

examples/tensorflow/translation/run_translation.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 # region Dependencies and constants
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

setup.py

@@ -453,7 +453,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.51.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.51.0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
     author_email="transformers@huggingface.co",
     description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",

src/transformers/__init__.py

@@ -18,7 +18,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.51.0.dev0"
+__version__ = "4.51.0"
 
 from typing import TYPE_CHECKING
 

src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py

@@ -1,62 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert ALBERT checkpoint."""
-
-import argparse
-
-import torch
-
-from ...utils import logging
-from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path):
-    # Initialise PyTorch model
-    config = AlbertConfig.from_json_file(albert_config_file)
-    print(f"Building PyTorch model from configuration: {config}")
-    model = AlbertForPreTraining(config)
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_albert(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    torch.save(model.state_dict(), pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--albert_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained ALBERT model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

src/transformers/models/align/convert_align_tf_to_hf.py

@@ -1,389 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert ALIGN checkpoints from the original repository."""
-
-import argparse
-import os
-
-import align
-import numpy as np
-import requests
-import tensorflow as tf
-import torch
-from PIL import Image
-from tokenizer import Tokenizer
-
-from transformers import (
-    AlignConfig,
-    AlignModel,
-    AlignProcessor,
-    BertConfig,
-    BertTokenizer,
-    EfficientNetConfig,
-    EfficientNetImageProcessor,
-)
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def preprocess(image):
-    image = tf.image.resize(image, (346, 346))
-    image = tf.image.crop_to_bounding_box(image, (346 - 289) // 2, (346 - 289) // 2, 289, 289)
-    return image
-
-
-def get_align_config():
-    vision_config = EfficientNetConfig.from_pretrained("google/efficientnet-b7")
-    vision_config.image_size = 289
-    vision_config.hidden_dim = 640
-    vision_config.id2label = {"0": "LABEL_0", "1": "LABEL_1"}
-    vision_config.label2id = {"LABEL_0": 0, "LABEL_1": 1}
-    vision_config.depthwise_padding = []
-
-    text_config = BertConfig()
-    config = AlignConfig.from_text_vision_configs(
-        text_config=text_config, vision_config=vision_config, projection_dim=640
-    )
-    return config
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-def get_processor():
-    image_processor = EfficientNetImageProcessor(
-        do_center_crop=True,
-        rescale_factor=1 / 127.5,
-        rescale_offset=True,
-        do_normalize=False,
-        include_top=False,
-        resample=Image.BILINEAR,
-    )
-    tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
-    tokenizer.model_max_length = 64
-    processor = AlignProcessor(image_processor=image_processor, tokenizer=tokenizer)
-    return processor
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def rename_keys(original_param_names):
-    # EfficientNet image encoder
-    block_names = [v.split("_")[0].split("block")[1] for v in original_param_names if v.startswith("block")]
-    block_names = list(set(block_names))
-    block_names = sorted(block_names)
-    num_blocks = len(block_names)
-    block_name_mapping = {b: str(i) for b, i in zip(block_names, range(num_blocks))}
-
-    rename_keys = []
-    rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight"))
-    rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight"))
-    rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias"))
-    rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean"))
-    rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var"))
-
-    for b in block_names:
-        hf_b = block_name_mapping[b]
-        rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight"))
-        rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight"))
-        rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias"))
-        rename_keys.append(
-            (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")
-        )
-        rename_keys.append(
-            (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")
-        )
-        rename_keys.append(
-            (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")
-        )
-        rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight"))
-        rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias"))
-        rename_keys.append(
-            (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")
-        )
-        rename_keys.append(
-            (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")
-        )
-
-        rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight"))
-        rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias"))
-        rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight"))
-        rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias"))
-        rename_keys.append(
-            (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")
-        )
-        rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight"))
-        rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias"))
-        rename_keys.append(
-            (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")
-        )
-        rename_keys.append(
-            (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")
-        )
-
-    key_mapping = {}
-    for item in rename_keys:
-        if item[0] in original_param_names:
-            key_mapping[item[0]] = "vision_model." + item[1]
-
-    # BERT text encoder
-    rename_keys = []
-    old = "tf_bert_model/bert"
-    new = "text_model"
-    for i in range(12):
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
-                f"{new}.encoder.layer.{i}.attention.self.query.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
-                f"{new}.encoder.layer.{i}.attention.self.query.bias",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
-                f"{new}.encoder.layer.{i}.attention.self.key.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
-                f"{new}.encoder.layer.{i}.attention.self.key.bias",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
-                f"{new}.encoder.layer.{i}.attention.self.value.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
-                f"{new}.encoder.layer.{i}.attention.self.value.bias",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
-                f"{new}.encoder.layer.{i}.attention.output.dense.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
-                f"{new}.encoder.layer.{i}.attention.output.dense.bias",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
-                f"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
-                f"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
-                f"{new}.encoder.layer.{i}.intermediate.dense.weight",
-            )
-        )
-        rename_keys.append(
-            (
-                f"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
-                f"{new}.encoder.layer.{i}.intermediate.dense.bias",
-            )
-        )
-        rename_keys.append(
-            (f"{old}/encoder/layer_._{i}/output/dense/kernel:0", f"{new}.encoder.layer.{i}.output.dense.weight")
-        )
-        rename_keys.append(
-            (f"{old}/encoder/layer_._{i}/output/dense/bias:0", f"{new}.encoder.layer.{i}.output.dense.bias")
-        )
-        rename_keys.append(
-            (f"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", f"{new}.encoder.layer.{i}.output.LayerNorm.weight")
-        )
-        rename_keys.append(
-            (f"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", f"{new}.encoder.layer.{i}.output.LayerNorm.bias")
-        )
-
-    rename_keys.append((f"{old}/embeddings/word_embeddings/weight:0", f"{new}.embeddings.word_embeddings.weight"))
-    rename_keys.append(
-        (f"{old}/embeddings/position_embeddings/embeddings:0", f"{new}.embeddings.position_embeddings.weight")
-    )
-    rename_keys.append(
-        (f"{old}/embeddings/token_type_embeddings/embeddings:0", f"{new}.embeddings.token_type_embeddings.weight")
-    )
-    rename_keys.append((f"{old}/embeddings/LayerNorm/gamma:0", f"{new}.embeddings.LayerNorm.weight"))
-    rename_keys.append((f"{old}/embeddings/LayerNorm/beta:0", f"{new}.embeddings.LayerNorm.bias"))
-
-    rename_keys.append((f"{old}/pooler/dense/kernel:0", f"{new}.pooler.dense.weight"))
-    rename_keys.append((f"{old}/pooler/dense/bias:0", f"{new}.pooler.dense.bias"))
-    rename_keys.append(("dense/kernel:0", "text_projection.weight"))
-    rename_keys.append(("dense/bias:0", "text_projection.bias"))
-    rename_keys.append(("dense/bias:0", "text_projection.bias"))
-    rename_keys.append(("temperature:0", "temperature"))
-
-    for item in rename_keys:
-        if item[0] in original_param_names:
-            key_mapping[item[0]] = item[1]
-    return key_mapping
-
-
-def replace_params(hf_params, tf_params, key_mapping):
-    list(hf_params.keys())
-
-    for key, value in tf_params.items():
-        if key not in key_mapping:
-            continue
-
-        hf_key = key_mapping[key]
-        if "_conv" in key and "kernel" in key:
-            new_hf_value = torch.from_numpy(value).permute(3, 2, 0, 1)
-        elif "embeddings" in key:
-            new_hf_value = torch.from_numpy(value)
-        elif "depthwise_kernel" in key:
-            new_hf_value = torch.from_numpy(value).permute(2, 3, 0, 1)
-        elif "kernel" in key:
-            new_hf_value = torch.from_numpy(np.transpose(value))
-        elif "temperature" in key:
-            new_hf_value = value
-        elif "bn/gamma" or "bn/beta" in key:
-            new_hf_value = torch.from_numpy(np.transpose(value)).squeeze()
-        else:
-            new_hf_value = torch.from_numpy(value)
-
-        # Replace HF parameters with original TF model parameters
-        hf_params[hf_key].copy_(new_hf_value)
-
-
-@torch.no_grad()
-def convert_align_checkpoint(checkpoint_path, pytorch_dump_folder_path, save_model, push_to_hub):
-    """
-    Copy/paste/tweak model's weights to our ALIGN structure.
-    """
-    # Load original model
-    seq_length = 64
-    tok = Tokenizer(seq_length)
-    original_model = align.Align("efficientnet-b7", "bert-base", 640, seq_length, tok.get_vocab_size())
-    original_model.compile()
-    original_model.load_weights(checkpoint_path)
-
-    tf_params = original_model.trainable_variables
-    tf_non_train_params = original_model.non_trainable_variables
-    tf_params = {param.name: param.numpy() for param in tf_params}
-    for param in tf_non_train_params:
-        tf_params[param.name] = param.numpy()
-    tf_param_names = list(tf_params.keys())
-
-    # Load HuggingFace model
-    config = get_align_config()
-    hf_model = AlignModel(config).eval()
-    hf_params = hf_model.state_dict()
-
-    # Create src-to-dst parameter name mapping dictionary
-    print("Converting parameters...")
-    key_mapping = rename_keys(tf_param_names)
-    replace_params(hf_params, tf_params, key_mapping)
-
-    # Initialize processor
-    processor = get_processor()
-    inputs = processor(
-        images=prepare_img(), text="A picture of a cat", padding="max_length", max_length=64, return_tensors="pt"
-    )
-
-    # HF model inference
-    hf_model.eval()
-    with torch.no_grad():
-        outputs = hf_model(**inputs)
-
-    hf_image_features = outputs.image_embeds.detach().numpy()
-    hf_text_features = outputs.text_embeds.detach().numpy()
-
-    # Original model inference
-    original_model.trainable = False
-    tf_image_processor = EfficientNetImageProcessor(
-        do_center_crop=True,
-        do_rescale=False,
-        do_normalize=False,
-        include_top=False,
-        resample=Image.BILINEAR,
-    )
-    image = tf_image_processor(images=prepare_img(), return_tensors="tf", data_format="channels_last")["pixel_values"]
-    text = tok(tf.constant(["A picture of a cat"]))
-
-    image_features = original_model.image_encoder(image, training=False)
-    text_features = original_model.text_encoder(text, training=False)
-
-    image_features = tf.nn.l2_normalize(image_features, axis=-1)
-    text_features = tf.nn.l2_normalize(text_features, axis=-1)
-
-    # Check whether original and HF model outputs match  -> np.allclose
-    if not np.allclose(image_features, hf_image_features, atol=1e-3):
-        raise ValueError("The predicted image features are not the same.")
-    if not np.allclose(text_features, hf_text_features, atol=1e-3):
-        raise ValueError("The predicted text features are not the same.")
-    print("Model outputs match!")
-
-    if save_model:
-        # Create folder to save model
-        if not os.path.isdir(pytorch_dump_folder_path):
-            os.mkdir(pytorch_dump_folder_path)
-        # Save converted model and image processor
-        hf_model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        # Push model and image processor to hub
-        print("Pushing converted ALIGN to the hub...")
-        processor.push_to_hub("align-base")
-        hf_model.push_to_hub("align-base")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--checkpoint_path",
-        default="./weights/model-weights",
-        type=str,
-        help="Path to the pretrained TF ALIGN checkpoint.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default="hf_model",
-        type=str,
-        help="Path to the output PyTorch model directory.",
-    )
-    parser.add_argument("--save_model", action="store_true", help="Save model to local")
-    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
-
-    args = parser.parse_args()
-    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)

src/transformers/models/aria/convert_aria_weights_to_hf.py

@@ -1,162 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import glob
-
-import torch
-from huggingface_hub import snapshot_download
-from safetensors import safe_open
-
-from transformers import (
-    AddedToken,
-    AriaForConditionalGeneration,
-    AriaProcessor,
-    AutoConfig,
-    AutoTokenizer,
-)
-
-
-EPILOG_TXT = """Example:
-    python transformers/src/transformers/models/aria/convert_aria_weights_to_hf.py --text_model_id rhymes-ai/Aria --vision_model_id rhymes-ai/Aria --output_hub_path m-ric/Aria_hf_2 --old_state_dict_id rhymes-ai/Aria
-
-Example for creating the old state dict file with Python:
-
-    import torch
-    from aria.model.language_model.aria_llama import AriaTextForCausalLM
-
-    # load model
-    kwargs = {"device_map": "auto", "torch_dtype": torch.float16}
-    model = AriaTextForCausalLM.from_pretrained("rhymes-ai/Aria", low_cpu_mem_usage=True, **kwargs)
-
-    # load vision tower
-    model.get_vision_tower().load_model()
-
-    # Save state dict
-    torch.save(model.state_dict(), "tmp/hf_models/aria/model_state_dict.bin")
-"""
-
-KEYS_TO_MODIFY_MAPPING = {
-    "vision_tower.vision_model": "vision_tower",
-    "ln_ffn": "layer_norm",
-    "ffn": "feed_forward",
-    "ln_kv": "layer_norm_kv",
-}
-
-
-def load_original_state_dict(model_id):
-    directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
-
-    original_state_dict = {}
-    for path in glob.glob(f"{directory_path}/*"):
-        if path.endswith(".safetensors"):
-            with safe_open(path, framework="pt", device="cpu") as f:
-                for key in f.keys():
-                    original_state_dict[key] = f.get_tensor(key)
-
-    return original_state_dict
-
-
-def convert_state_dict_to_hf(state_dict):
-    new_state_dict = {}
-    for key, value in state_dict.items():
-        if key.endswith(".inv_freq"):
-            continue
-        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
-            if key_to_modify in key:
-                key = key.replace(key_to_modify, new_key)
-
-        new_state_dict[key] = value
-    new_state_dict["vision_tower.post_layernorm.weight"] = torch.zeros((1152,))
-    new_state_dict["vision_tower.post_layernorm.bias"] = torch.zeros((1152,))
-
-    return new_state_dict
-
-
-def convert_aria_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
-    torch.set_default_dtype(torch.float16)
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        text_model_id,
-        extra_special_tokens={
-            "image_token": "<|img|>",
-            "pad_token": "<pad>",
-        },
-    )
-    tokenizer.add_tokens(AddedToken("<|img|>", special=True, normalized=False), special_tokens=True)
-    tokenizer.add_special_tokens({"pad_token": "<pad>"})
-    tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}{% elif message['content'] is iterable %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<fim_prefix><|img|><fim_suffix>{% endif %}{% endfor %}{% endif %}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
-
-    processor = AriaProcessor.from_pretrained(
-        text_model_id,
-        tokenizer=tokenizer,
-    )
-
-    config = AutoConfig.from_pretrained(text_model_id)
-    config.vision_config.hidden_size = 1152
-    config.vision_config.attention_heads = 16
-    config.pad_token_id = 2
-    config.image_token_index = 9
-    config.intermediate_size = config.moe_intermediate_size
-    config.auto_map = {
-        "AutoConfig": "modeling_aria.AriaConfig",
-        "AutoModelForCausalLM": "modeling_aria.AriaForConditionalGeneration",
-    }
-
-    with torch.device("meta"):
-        model = AriaForConditionalGeneration(config)
-
-    state_dict = load_original_state_dict(old_state_dict_id)
-
-    state_dict = convert_state_dict_to_hf(state_dict)
-    model.load_state_dict(state_dict, strict=False, assign=True)
-
-    # print("Saving models")
-    # model.save_pretrained("local_aria", safe_serialization=False)
-    # processor.save_pretrained("local_aria")
-    print("Pushing to hub")
-    model.push_to_hub(output_hub_path, create_pr=True)
-    processor.push_to_hub(output_hub_path, create_pr=True)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        epilog=EPILOG_TXT,
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    parser.add_argument(
-        "--text_model_id",
-        default="rhymes-ai/Aria",
-        help="Hub location of the text model",
-    )
-    parser.add_argument(
-        "--vision_model_id",
-        default="rhymes-ai/Aria",
-        help="Hub location of the vision model",
-    )
-    parser.add_argument(
-        "--output_hub_path",
-        default="rhymes-ai/Aria",
-        help="Location on the hub of the converted model",
-    )
-    parser.add_argument(
-        "--old_state_dict_id",
-        default="rhymes-ai/Aria",
-        help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
-    )
-    args = parser.parse_args()
-    convert_aria_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id)
-
-
-if __name__ == "__main__":
-    main()

src/transformers/models/audio_spectrogram_transformer/convert_audio_spectrogram_transformer_original_to_pytorch.py

@@ -1,279 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Audio Spectrogram Transformer checkpoints from the original repository. URL: https://github.com/YuanGongND/ast"""
-
-import argparse
-import json
-from pathlib import Path
-
-import torch
-import torchaudio
-from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-
-from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_audio_spectrogram_transformer_config(model_name):
-    config = ASTConfig()
-
-    if "10-10" in model_name:
-        pass
-    elif "speech-commands" in model_name:
-        config.max_length = 128
-    elif "12-12" in model_name:
-        config.time_stride = 12
-        config.frequency_stride = 12
-    elif "14-14" in model_name:
-        config.time_stride = 14
-        config.frequency_stride = 14
-    elif "16-16" in model_name:
-        config.time_stride = 16
-        config.frequency_stride = 16
-    else:
-        raise ValueError("Model not supported")
-
-    repo_id = "huggingface/label-files"
-    if "speech-commands" in model_name:
-        config.num_labels = 35
-        filename = "speech-commands-v2-id2label.json"
-    else:
-        config.num_labels = 527
-        filename = "audioset-id2label.json"
-
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    return config
-
-
-def rename_key(name):
-    if "module.v" in name:
-        name = name.replace("module.v", "audio_spectrogram_transformer")
-    if "cls_token" in name:
-        name = name.replace("cls_token", "embeddings.cls_token")
-    if "dist_token" in name:
-        name = name.replace("dist_token", "embeddings.distillation_token")
-    if "pos_embed" in name:
-        name = name.replace("pos_embed", "embeddings.position_embeddings")
-    if "patch_embed.proj" in name:
-        name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
-    # transformer blocks
-    if "blocks" in name:
-        name = name.replace("blocks", "encoder.layer")
-    if "attn.proj" in name:
-        name = name.replace("attn.proj", "attention.output.dense")
-    if "attn" in name:
-        name = name.replace("attn", "attention.self")
-    if "norm1" in name:
-        name = name.replace("norm1", "layernorm_before")
-    if "norm2" in name:
-        name = name.replace("norm2", "layernorm_after")
-    if "mlp.fc1" in name:
-        name = name.replace("mlp.fc1", "intermediate.dense")
-    if "mlp.fc2" in name:
-        name = name.replace("mlp.fc2", "output.dense")
-    # final layernorm
-    if "audio_spectrogram_transformer.norm" in name:
-        name = name.replace("audio_spectrogram_transformer.norm", "audio_spectrogram_transformer.layernorm")
-    # classifier head
-    if "module.mlp_head.0" in name:
-        name = name.replace("module.mlp_head.0", "classifier.layernorm")
-    if "module.mlp_head.1" in name:
-        name = name.replace("module.mlp_head.1", "classifier.dense")
-
-    return name
-
-
-def convert_state_dict(orig_state_dict, config):
-    for key in orig_state_dict.copy().keys():
-        val = orig_state_dict.pop(key)
-
-        if "qkv" in key:
-            key_split = key.split(".")
-            layer_num = int(key_split[3])
-            dim = config.hidden_size
-            if "weight" in key:
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.weight"
-                ] = val[:dim, :]
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.weight"
-                ] = val[dim : dim * 2, :]
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.weight"
-                ] = val[-dim:, :]
-            else:
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.bias"
-                ] = val[:dim]
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.bias"
-                ] = val[dim : dim * 2]
-                orig_state_dict[
-                    f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.bias"
-                ] = val[-dim:]
-        else:
-            orig_state_dict[rename_key(key)] = val
-
-    return orig_state_dict
-
-
-def remove_keys(state_dict):
-    ignore_keys = [
-        "module.v.head.weight",
-        "module.v.head.bias",
-        "module.v.head_dist.weight",
-        "module.v.head_dist.bias",
-    ]
-    for k in ignore_keys:
-        state_dict.pop(k, None)
-
-
-@torch.no_grad()
-def convert_audio_spectrogram_transformer_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
-    """
-    Copy/paste/tweak model's weights to our Audio Spectrogram Transformer structure.
-    """
-    config = get_audio_spectrogram_transformer_config(model_name)
-
-    model_name_to_url = {
-        "ast-finetuned-audioset-10-10-0.4593": (
-            "https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-10-10-0.450": (
-            "https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-10-10-0.448": (
-            "https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-10-10-0.448-v2": (
-            "https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-12-12-0.447": (
-            "https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-14-14-0.443": (
-            "https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
-        ),
-        "ast-finetuned-audioset-16-16-0.442": (
-            "https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
-        ),
-        "ast-finetuned-speech-commands-v2": (
-            "https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
-        ),
-    }
-
-    # load original state_dict
-    checkpoint_url = model_name_to_url[model_name]
-    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
-    # remove some keys
-    remove_keys(state_dict)
-    # rename some keys
-    new_state_dict = convert_state_dict(state_dict, config)
-
-    # load 🤗 model
-    model = ASTForAudioClassification(config)
-    model.eval()
-
-    model.load_state_dict(new_state_dict)
-
-    # verify outputs on dummy input
-    # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
-    mean = -4.2677393 if "speech-commands" not in model_name else -6.845978
-    std = 4.5689974 if "speech-commands" not in model_name else 5.5654526
-    max_length = 1024 if "speech-commands" not in model_name else 128
-    feature_extractor = ASTFeatureExtractor(mean=mean, std=std, max_length=max_length)
-
-    if "speech-commands" in model_name:
-        # TODO: Convert dataset to Parquet
-        dataset = load_dataset("google/speech_commands", "v0.02", split="validation", trust_remote_code=True)
-        waveform = dataset[0]["audio"]["array"]
-    else:
-        filepath = hf_hub_download(
-            repo_id="nielsr/audio-spectogram-transformer-checkpoint",
-            filename="sample_audio.flac",
-            repo_type="dataset",
-        )
-
-        waveform, _ = torchaudio.load(filepath)
-        waveform = waveform.squeeze().numpy()
-
-    inputs = feature_extractor(waveform, sampling_rate=16000, return_tensors="pt")
-
-    # forward pass
-    outputs = model(**inputs)
-    logits = outputs.logits
-
-    if model_name == "ast-finetuned-audioset-10-10-0.4593":
-        expected_slice = torch.tensor([-0.8760, -7.0042, -8.6602])
-    elif model_name == "ast-finetuned-audioset-10-10-0.450":
-        expected_slice = torch.tensor([-1.1986, -7.0903, -8.2718])
-    elif model_name == "ast-finetuned-audioset-10-10-0.448":
-        expected_slice = torch.tensor([-2.6128, -8.0080, -9.4344])
-    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
-        expected_slice = torch.tensor([-1.5080, -7.4534, -8.8917])
-    elif model_name == "ast-finetuned-audioset-12-12-0.447":
-        expected_slice = torch.tensor([-0.5050, -6.5833, -8.0843])
-    elif model_name == "ast-finetuned-audioset-14-14-0.443":
-        expected_slice = torch.tensor([-0.3826, -7.0336, -8.2413])
-    elif model_name == "ast-finetuned-audioset-16-16-0.442":
-        expected_slice = torch.tensor([-1.2113, -6.9101, -8.3470])
-    elif model_name == "ast-finetuned-speech-commands-v2":
-        expected_slice = torch.tensor([6.1589, -8.0566, -8.7984])
-    else:
-        raise ValueError("Unknown model name")
-    if not torch.allclose(logits[0, :3], expected_slice, atol=1e-4):
-        raise ValueError("Logits don't match")
-    print("Looks ok!")
-
-    if pytorch_dump_folder_path is not None:
-        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
-        model.save_pretrained(pytorch_dump_folder_path)
-        print(f"Saving feature extractor to {pytorch_dump_folder_path}")
-        feature_extractor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print("Pushing model and feature extractor to the hub...")
-        model.push_to_hub(f"MIT/{model_name}")
-        feature_extractor.push_to_hub(f"MIT/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="ast-finetuned-audioset-10-10-0.4593",
-        type=str,
-        help="Name of the Audio Spectrogram Transformer model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-
-    args = parser.parse_args()
-    convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/bamba/convert_mamba_ssm_checkpoint.py

@@ -1,273 +0,0 @@
-# coding=utf-8
-# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""This script can be used to convert checkpoints provided in the `mamba_ssm` library into the format provided in HuggingFace `transformers`. It depends on the `mamba2_ssm` package to be installed."""
-
-import argparse
-import json
-import os
-import re
-from os import path
-from typing import Dict, Optional, Union
-
-import torch
-from huggingface_hub import split_torch_state_dict_into_shards
-from safetensors.torch import save_file
-
-from transformers import AutoTokenizer
-from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME
-
-from .configuration_bamba import BambaConfig
-
-
-def convert_state_dict_from_mamba_ssm(original_sd: Dict) -> Dict[str, torch.Tensor]:
-    state_dict = {}
-
-    for orig_k, param in original_sd.items():
-        k = orig_k.replace("backbone", "model")
-
-        # for embeddings
-        k = k.replace("embedding", "embed_tokens")
-
-        # for mixer
-        k = k.replace("mixer", "mamba")
-
-        # for final layernorm
-        k = k.replace("norm_f", "final_layernorm")
-
-        # for block layernorm
-        k = re.sub(r"(\d+)\.norm\.", r"\1.input_layernorm.", k)
-        k = re.sub(r"(\d+)\.norm2\.", r"\1.pre_ff_layernorm.", k)
-
-        # for mlp
-        k = k.replace("mlp.fc2", "feed_forward.down_proj")
-
-        if "mlp.fc1" in k:
-            param, param2 = torch.chunk(param, 2, dim=0)
-            k2 = k.replace("mlp.fc1", "feed_forward.gate_proj")
-            state_dict[k2] = param2
-            k = k.replace("mlp.fc1", "feed_forward.up_proj")
-
-        if ("in_proj" in k and orig_k.replace("in_proj", "conv1d") in original_sd) or (
-            "out_proj" in k and orig_k.replace("out_proj", "conv1d") in original_sd
-        ):
-            # then this must be a mamba
-            pass
-        else:
-            # for attn
-            # - because mixer was replaced to mamba above
-            k = k.replace("mamba.out_proj", "self_attn.o_proj")
-            if "mamba.in_proj" in k:
-                m, n = param.shape
-                d = (m - n) // 2
-                param, param2, param3 = torch.split(param, [n, d, d], dim=0)
-                k2 = k.replace("mamba.in_proj", "self_attn.k_proj")
-                state_dict[k2] = param2
-                k2 = k.replace("mamba.in_proj", "self_attn.v_proj")
-                state_dict[k2] = param3
-                k = k.replace("mamba.in_proj", "self_attn.q_proj")
-
-        state_dict[k] = param
-
-    return state_dict
-
-
-# Adapted from transformers.models.mamba.convert_mamba_ssm_checkpoint_to_pytorch.py
-def convert_ssm_config_to_hf_config(
-    config_ssm: Dict,
-    **kwargs,
-) -> BambaConfig:
-    """Convert a config from mamba_ssm to a BambaConfig from here."""
-    hf_config: BambaConfig = BambaConfig(**kwargs)
-
-    hf_config.architectures = ["BambaForCausalLM"]
-
-    # Set important values from config and recalculate other resulting entries
-    hf_config.hidden_size = config_ssm["d_model"]
-    hf_config.intermediate_size = config_ssm["d_intermediate"]
-    hf_config.mamba_n_heads = (hf_config.hidden_size * hf_config.mamba_expand) // hf_config.mamba_d_head
-    hf_config.num_hidden_layers = config_ssm["n_layer"]
-    hf_config.tie_word_embeddings = config_ssm["tie_embeddings"]
-
-    # currently this script assumes config_ssm belongs to v2
-    if config_ssm["ssm_cfg"].get("layer") != "Mamba2":
-        raise ValueError("Conversion script only supports Mamba2")
-
-    # Set attention values
-    attn_cfg = config_ssm.get("attn_cfg")
-    if attn_cfg:
-        assert attn_cfg["causal"], "Only support non-causal attention."
-        assert not attn_cfg["qkv_proj_bias"], "Only support no qkv bias."
-        assert not attn_cfg["out_proj_bias"], "Only support no out bias."
-        hf_config.attn_rotary_emb = attn_cfg["rotary_emb_dim"]
-        hf_config.num_attention_heads = attn_cfg["num_heads"]
-        hf_config.num_key_value_heads = attn_cfg["num_heads_kv"]
-
-    attention_layer_indices = config_ssm.get("attn_layer_idx")
-    if attention_layer_indices:
-        hf_config.attn_layer_indices = attention_layer_indices
-
-    # Padded vocab size, mostly of 16 but 32 is also very common in different models
-    vocab_size = config_ssm["vocab_size"]
-    pad_vocab_size_multiple = config_ssm["pad_vocab_size_multiple"]
-    if (vocab_size % pad_vocab_size_multiple) != 0:
-        vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
-    hf_config.vocab_size = vocab_size
-
-    return hf_config
-
-
-def save_single_safetensor(
-    state_dict: Dict,
-    save_directory: str,
-    metadata: Dict,
-):
-    save_file(
-        state_dict,
-        os.path.join(save_directory, SAFE_WEIGHTS_NAME),
-        metadata,
-    )
-
-
-def save_sharded_safetensors(
-    state_dict: Dict,
-    save_directory: str,
-    metadata: Dict,
-    max_shard_size: Union[int, str] = "5GB",
-):
-    filename_pattern = SAFE_WEIGHTS_NAME.replace(".bin", "{suffix}.bin").replace(
-        ".safetensors", "{suffix}.safetensors"
-    )
-    state_dict_split = split_torch_state_dict_into_shards(
-        state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
-    )
-    index = {
-        "metadata": state_dict_split.metadata,
-        "weight_map": state_dict_split.tensor_to_filename,
-    }
-    # Save the index
-    with open(os.path.join(save_directory, SAFE_WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f:
-        content = json.dumps(index, indent=2, sort_keys=True) + "\n"
-        f.write(content)
-
-    filename_to_tensors = state_dict_split.filename_to_tensors.items()
-    for shard_file, tensors in filename_to_tensors:
-        shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
-        save_file(shard, os.path.join(save_directory, shard_file), metadata=metadata)
-
-
-# Adapted from transformers.models.mamba.convert_mamba_ssm_checkpoint_to_pytorch.py
-def convert_mamba_ssm_checkpoint_file_to_huggingface_model_file(
-    mamba_ssm_checkpoint_path: str,
-    precision: str,
-    output_dir: str,
-    tokenizer_path: Optional[str] = None,
-    save_model: Union[bool, str] = True,
-) -> None:
-    # load tokenizer if provided, this will be used to set the
-    # token_ids in the config file
-    token_ids = {}
-    if tokenizer_path:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-        for key in [
-            "bos_token_id",
-            "eos_token_id",
-            "pad_token_id",
-        ]:
-            id = getattr(tokenizer, key, None)
-            if id:
-                token_ids[key] = id
-
-    # there are some configs unsettable by mamba_ssn config, so
-    # if there are changes from the defaults, have to pass them into
-    # the function
-    unsettables = {
-        "mamba_d_head": 64,
-        "mamba_d_state": 128,
-        "mamba_n_groups": 1,
-        "rms_norm_eps": 1e-5,
-    }
-
-    # Load and save config based on name
-    config_path = path.join(mamba_ssm_checkpoint_path, "config.json")
-    with open(config_path, "r", encoding="utf-8") as json_file:
-        config = json.load(json_file)
-
-    # convert the config
-    hf_config = convert_ssm_config_to_hf_config(
-        config_ssm=config,
-        **token_ids,
-        **unsettables,
-    )
-    hf_config.save_pretrained(output_dir)
-
-    # Load state dict of the original model and transfer to hf model
-    state_dict = torch.load(
-        path.join(mamba_ssm_checkpoint_path, "pytorch_model.bin"),
-        map_location="cpu",
-        weights_only=True,
-    )
-    # FIXME: allow other parameters to pass in
-    state_dict = convert_state_dict_from_mamba_ssm(state_dict)
-
-    # Save new model to pytorch_dump_path
-    dtype = torch.float32 if precision == "fp32" else (torch.bfloat16 if precision == "bf16" else torch.float16)
-
-    save_file_fn = None
-    if isinstance(save_model, bool) and save_model:
-        save_file_fn = save_single_safetensor
-    elif isinstance(save_model, str) and save_model == "sharded":
-        save_file_fn = save_sharded_safetensors
-
-    if save_file_fn:
-        save_file_fn({k: v.to(dtype) for k, v in state_dict.items()}, output_dir, metadata={"format": "pt"})
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-i",
-        "--mamba_ssm_checkpoint_directory",
-        type=str,
-        required=True,
-        help="Path to a directory containing the `pytorch_model.bin` mamba_ssm checkpoint file to be converted.",
-    )
-    parser.add_argument(
-        "-p",
-        "--precision",
-        type=str,
-        default="fp16",
-        const="fp16",
-        required=True,
-        choices=("fp32", "fp16", "bf16"),
-        help="The precision the model will be saved in. Select from fp32, fp16 or bf16.",
-    )
-    parser.add_argument(
-        "-o", "--output_dir", type=str, required=True, help="Path to directory to save the converted output model to."
-    )
-    parser.add_argument(
-        "-t",
-        "--tokenizer_model_path",
-        type=str,
-        default=None,
-        required=False,
-        help="Path to a the tokenizer file.",
-    )
-    args = parser.parse_args()
-
-    convert_mamba_ssm_checkpoint_file_to_huggingface_model_file(
-        args.mamba2_checkpoint_directory,
-        args.precision,
-        args.output_dir,
-    )

src/transformers/models/bark/convert_suno_to_hf.py

@@ -1,263 +0,0 @@
-"""Convert Bark checkpoint."""
-
-import argparse
-import os
-from pathlib import Path
-
-import torch
-from bark.generation import _load_model as _bark_load_model
-from huggingface_hub import hf_hub_download
-
-from transformers import EncodecConfig, EncodecModel, set_seed
-from transformers.models.bark.configuration_bark import (
-    BarkCoarseConfig,
-    BarkConfig,
-    BarkFineConfig,
-    BarkSemanticConfig,
-)
-from transformers.models.bark.generation_configuration_bark import (
-    BarkCoarseGenerationConfig,
-    BarkFineGenerationConfig,
-    BarkGenerationConfig,
-    BarkSemanticGenerationConfig,
-)
-from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-set_seed(770)
-
-
-new_layer_name_dict = {
-    "c_attn": "att_proj",
-    "c_proj": "out_proj",
-    "c_fc": "in_proj",
-    "transformer.": "",
-    "h.": "layers.",
-    "ln_1": "layernorm_1",
-    "ln_2": "layernorm_2",
-    "ln_f": "layernorm_final",
-    "wpe": "position_embeds_layer",
-    "wte": "input_embeds_layer",
-}
-
-
-REMOTE_MODEL_PATHS = {
-    "text_small": {
-        "repo_id": "suno/bark",
-        "file_name": "text.pt",
-    },
-    "coarse_small": {
-        "repo_id": "suno/bark",
-        "file_name": "coarse.pt",
-    },
-    "fine_small": {
-        "repo_id": "suno/bark",
-        "file_name": "fine.pt",
-    },
-    "text": {
-        "repo_id": "suno/bark",
-        "file_name": "text_2.pt",
-    },
-    "coarse": {
-        "repo_id": "suno/bark",
-        "file_name": "coarse_2.pt",
-    },
-    "fine": {
-        "repo_id": "suno/bark",
-        "file_name": "fine_2.pt",
-    },
-}
-
-CUR_PATH = os.path.dirname(os.path.abspath(__file__))
-default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache")
-CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
-
-
-def _get_ckpt_path(model_type, use_small=False):
-    key = model_type
-    if use_small:
-        key += "_small"
-    return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"])
-
-
-def _download(from_hf_path, file_name):
-    os.makedirs(CACHE_DIR, exist_ok=True)
-    hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR)
-
-
-def _load_model(ckpt_path, device, use_small=False, model_type="text"):
-    if model_type == "text":
-        ModelClass = BarkSemanticModel
-        ConfigClass = BarkSemanticConfig
-        GenerationConfigClass = BarkSemanticGenerationConfig
-    elif model_type == "coarse":
-        ModelClass = BarkCoarseModel
-        ConfigClass = BarkCoarseConfig
-        GenerationConfigClass = BarkCoarseGenerationConfig
-    elif model_type == "fine":
-        ModelClass = BarkFineModel
-        ConfigClass = BarkFineConfig
-        GenerationConfigClass = BarkFineGenerationConfig
-    else:
-        raise NotImplementedError()
-    model_key = f"{model_type}_small" if use_small else model_type
-    model_info = REMOTE_MODEL_PATHS[model_key]
-    if not os.path.exists(ckpt_path):
-        logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
-        _download(model_info["repo_id"], model_info["file_name"])
-    checkpoint = torch.load(ckpt_path, map_location=device)
-    # this is a hack
-    model_args = checkpoint["model_args"]
-    if "input_vocab_size" not in model_args:
-        model_args["input_vocab_size"] = model_args["vocab_size"]
-        model_args["output_vocab_size"] = model_args["vocab_size"]
-        del model_args["vocab_size"]
-
-    # convert Bark model arguments to HF Bark model arguments
-    model_args["num_heads"] = model_args.pop("n_head")
-    model_args["hidden_size"] = model_args.pop("n_embd")
-    model_args["num_layers"] = model_args.pop("n_layer")
-
-    model_config = ConfigClass(**checkpoint["model_args"])
-    model = ModelClass(config=model_config)
-    model_generation_config = GenerationConfigClass()
-
-    model.generation_config = model_generation_config
-    state_dict = checkpoint["model"]
-    # fixup checkpoint
-    unwanted_prefix = "_orig_mod."
-    for k, v in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            # replace part of the key with corresponding layer name in HF implementation
-            new_k = k[len(unwanted_prefix) :]
-            for old_layer_name in new_layer_name_dict:
-                new_k = new_k.replace(old_layer_name, new_layer_name_dict[old_layer_name])
-
-            state_dict[new_k] = state_dict.pop(k)
-
-    extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
-    extra_keys = {k for k in extra_keys if not k.endswith(".attn.bias")}
-    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
-    missing_keys = {k for k in missing_keys if not k.endswith(".attn.bias")}
-    if len(extra_keys) != 0:
-        raise ValueError(f"extra keys found: {extra_keys}")
-    if len(missing_keys) != 0:
-        raise ValueError(f"missing keys: {missing_keys}")
-    model.load_state_dict(state_dict, strict=False)
-    n_params = model.num_parameters(exclude_embeddings=True)
-    val_loss = checkpoint["best_val_loss"].item()
-    logger.info(f"model loaded: {round(n_params / 1e6, 1)}M params, {round(val_loss, 3)} loss")
-    model.eval()
-    model.to(device)
-    del checkpoint, state_dict
-
-    return model
-
-
-def load_model(pytorch_dump_folder_path, use_small=False, model_type="text"):
-    if model_type not in ("text", "coarse", "fine"):
-        raise NotImplementedError()
-
-    device = "cpu"  # do conversion on cpu
-
-    ckpt_path = _get_ckpt_path(model_type, use_small=use_small)
-    model = _load_model(ckpt_path, device, model_type=model_type, use_small=use_small)
-
-    # load bark initial model
-    bark_model = _bark_load_model(ckpt_path, "cpu", model_type=model_type, use_small=use_small)
-
-    if model_type == "text":
-        bark_model = bark_model["model"]
-
-    if model.num_parameters(exclude_embeddings=True) != bark_model.get_num_params():
-        raise ValueError("initial and new models don't have the same number of parameters")
-
-    # check if same output as the bark model
-    batch_size = 5
-    sequence_length = 10
-
-    if model_type in ["text", "coarse"]:
-        vec = torch.randint(256, (batch_size, sequence_length), dtype=torch.int)
-        output_old_model = bark_model(vec)[0]
-
-        output_new_model_total = model(vec)
-
-        # take last logits
-        output_new_model = output_new_model_total.logits[:, [-1], :]
-
-    else:
-        prediction_codebook_channel = 3
-        n_codes_total = 8
-        vec = torch.randint(256, (batch_size, sequence_length, n_codes_total), dtype=torch.int)
-
-        output_new_model_total = model(prediction_codebook_channel, vec)
-        output_old_model = bark_model(prediction_codebook_channel, vec)
-
-        output_new_model = output_new_model_total.logits
-
-    # output difference should come from the difference of self-attention implementation design
-    if output_new_model.shape != output_old_model.shape:
-        raise ValueError("initial and new outputs don't have the same shape")
-    if (output_new_model - output_old_model).abs().max().item() > 1e-3:
-        raise ValueError("initial and new outputs are not equal")
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-
-
-def load_whole_bark_model(
-    semantic_path,
-    coarse_path,
-    fine_path,
-    append_text,
-    hub_path,
-    folder_path,
-):
-    pytorch_dump_folder_path = os.path.join(folder_path, append_text)
-
-    semanticConfig = BarkSemanticConfig.from_pretrained(os.path.join(semantic_path, "config.json"))
-    coarseAcousticConfig = BarkCoarseConfig.from_pretrained(os.path.join(coarse_path, "config.json"))
-    fineAcousticConfig = BarkFineConfig.from_pretrained(os.path.join(fine_path, "config.json"))
-    codecConfig = EncodecConfig.from_pretrained("facebook/encodec_24khz")
-
-    semantic = BarkSemanticModel.from_pretrained(semantic_path)
-    coarseAcoustic = BarkCoarseModel.from_pretrained(coarse_path)
-    fineAcoustic = BarkFineModel.from_pretrained(fine_path)
-    codec = EncodecModel.from_pretrained("facebook/encodec_24khz")
-
-    bark_config = BarkConfig.from_sub_model_configs(
-        semanticConfig, coarseAcousticConfig, fineAcousticConfig, codecConfig
-    )
-
-    bark_generation_config = BarkGenerationConfig.from_sub_model_configs(
-        semantic.generation_config, coarseAcoustic.generation_config, fineAcoustic.generation_config
-    )
-
-    bark = BarkModel(bark_config)
-
-    bark.semantic = semantic
-    bark.coarse_acoustics = coarseAcoustic
-    bark.fine_acoustics = fineAcoustic
-    bark.codec_model = codec
-
-    bark.generation_config = bark_generation_config
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    bark.save_pretrained(pytorch_dump_folder_path, repo_id=hub_path, push_to_hub=True)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-
-    parser.add_argument("model_type", type=str, help="text, coarse or fine.")
-    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
-
-    args = parser.parse_args()
-
-    load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)

src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py

@@ -1,156 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BART checkpoint."""
-
-import argparse
-import os
-from pathlib import Path
-
-import fairseq
-import torch
-from packaging import version
-from torch import nn
-
-from transformers import (
-    BartConfig,
-    BartForConditionalGeneration,
-    BartForSequenceClassification,
-    BartModel,
-    BartTokenizer,
-)
-from transformers.utils import logging
-
-
-FAIRSEQ_MODELS = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
-extra_arch = {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
-if version.parse(fairseq.__version__) < version.parse("0.9.0"):
-    raise Exception("requires fairseq >= 0.9.0")
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-SAMPLE_TEXT = " Hello world! cécé herlolip"
-
-mnli_rename_keys = [
-    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
-    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
-    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
-    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
-]
-
-
-def remove_ignore_keys_(state_dict):
-    ignore_keys = [
-        "encoder.version",
-        "decoder.version",
-        "model.encoder.version",
-        "model.decoder.version",
-        "_float_tensor",
-    ]
-    for k in ignore_keys:
-        state_dict.pop(k, None)
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-def load_xsum_checkpoint(checkpoint_path):
-    """Checkpoint path should end in model.pt"""
-    sd = torch.load(checkpoint_path, map_location="cpu")
-    hub_interface = torch.hub.load("pytorch/fairseq", "bart.large.cnn").eval()
-    hub_interface.model.load_state_dict(sd["model"])
-    return hub_interface
-
-
-def make_linear_from_emb(emb):
-    vocab_size, emb_size = emb.weight.shape
-    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
-    lin_layer.weight.data = emb.weight.data
-    return lin_layer
-
-
-@torch.no_grad()
-def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkpoint_name=None):
-    """
-    Copy/paste/tweak model's weights to our BERT structure.
-    """
-    if not os.path.exists(checkpoint_path):
-        bart = torch.hub.load("pytorch/fairseq", checkpoint_path).eval()
-    else:
-        bart = load_xsum_checkpoint(checkpoint_path)
-
-    bart.model.upgrade_state_dict(bart.model.state_dict())
-    if hf_checkpoint_name is None:
-        hf_checkpoint_name = checkpoint_path.replace(".", "-")
-    config = BartConfig.from_pretrained(hf_checkpoint_name)
-    tokens = bart.encode(SAMPLE_TEXT).unsqueeze(0)
-    tokens2 = BartTokenizer.from_pretrained(hf_checkpoint_name).encode(SAMPLE_TEXT, return_tensors="pt").unsqueeze(0)
-    if not torch.eq(tokens, tokens2).all():
-        raise ValueError(
-            f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokens2}"
-        )
-
-    if checkpoint_path == "bart.large.mnli":
-        state_dict = bart.state_dict()
-        remove_ignore_keys_(state_dict)
-        state_dict["model.shared.weight"] = state_dict["model.decoder.embed_tokens.weight"]
-        for src, dest in mnli_rename_keys:
-            rename_key(state_dict, src, dest)
-        model = BartForSequenceClassification(config).eval()
-        model.load_state_dict(state_dict)
-        fairseq_output = bart.predict("mnli", tokens, return_logits=True)
-        new_model_outputs = model(tokens)[0]  # logits
-    else:  # no classification heads to worry about
-        state_dict = bart.model.state_dict()
-        remove_ignore_keys_(state_dict)
-        state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
-        fairseq_output = bart.extract_features(tokens)
-        if hf_checkpoint_name == "facebook/bart-large":
-            model = BartModel(config).eval()
-            model.load_state_dict(state_dict)
-            new_model_outputs = model(tokens).model[0]
-        else:
-            model = BartForConditionalGeneration(config).eval()  # an existing summarization ckpt
-            model.model.load_state_dict(state_dict)
-            if hasattr(model, "lm_head"):
-                model.lm_head = make_linear_from_emb(model.model.shared)
-            new_model_outputs = model.model(tokens)[0]
-
-    # Check results
-    if fairseq_output.shape != new_model_outputs.shape:
-        raise ValueError(
-            f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"
-        )
-    if (fairseq_output != new_model_outputs).any().item():
-        raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`")
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
-    )
-    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument(
-        "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
-    )
-    args = parser.parse_args()
-    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)

src/transformers/models/beit/convert_beit_unilm_to_pytorch.py

@@ -1,373 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BEiT checkpoints from the unilm repository."""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import (
-    BeitConfig,
-    BeitForImageClassification,
-    BeitForMaskedImageModeling,
-    BeitForSemanticSegmentation,
-    BeitImageProcessor,
-)
-from transformers.image_utils import PILImageResampling
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def create_rename_keys(config, has_lm_head=False, is_semantic=False):
-    prefix = "backbone." if is_semantic else ""
-
-    rename_keys = []
-    for i in range(config.num_hidden_layers):
-        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
-        rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight"))
-        rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias"))
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")
-        )
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")
-        )
-        rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight"))
-        rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias"))
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight"))
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias"))
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight"))
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias"))
-
-    # projection layer + position embeddings
-    rename_keys.extend(
-        [
-            (f"{prefix}cls_token", "beit.embeddings.cls_token"),
-            (f"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
-            (f"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
-        ]
-    )
-
-    if has_lm_head:
-        # mask token + shared relative position bias + layernorm
-        rename_keys.extend(
-            [
-                ("mask_token", "beit.embeddings.mask_token"),
-                (
-                    "rel_pos_bias.relative_position_bias_table",
-                    "beit.encoder.relative_position_bias.relative_position_bias_table",
-                ),
-                (
-                    "rel_pos_bias.relative_position_index",
-                    "beit.encoder.relative_position_bias.relative_position_index",
-                ),
-                ("norm.weight", "layernorm.weight"),
-                ("norm.bias", "layernorm.bias"),
-            ]
-        )
-    elif is_semantic:
-        # semantic segmentation classification heads
-        rename_keys.extend(
-            [
-                ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
-                ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
-                ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
-                ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
-            ]
-        )
-    else:
-        # layernorm + classification head
-        rename_keys.extend(
-            [
-                ("fc_norm.weight", "beit.pooler.layernorm.weight"),
-                ("fc_norm.bias", "beit.pooler.layernorm.bias"),
-                ("head.weight", "classifier.weight"),
-                ("head.bias", "classifier.bias"),
-            ]
-        )
-
-    return rename_keys
-
-
-# we split up the matrix of each encoder layer into queries, keys and values
-def read_in_q_k_v(state_dict, config, has_lm_head=False, is_semantic=False):
-    for i in range(config.num_hidden_layers):
-        prefix = "backbone." if is_semantic else ""
-        # queries, keys and values
-        in_proj_weight = state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight")
-        q_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias")
-        v_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias")
-
-        state_dict[f"beit.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
-            : config.hidden_size, :
-        ]
-        state_dict[f"beit.encoder.layer.{i}.attention.attention.query.bias"] = q_bias
-        state_dict[f"beit.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
-            config.hidden_size : config.hidden_size * 2, :
-        ]
-        state_dict[f"beit.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-            -config.hidden_size :, :
-        ]
-        state_dict[f"beit.encoder.layer.{i}.attention.attention.value.bias"] = v_bias
-
-        # gamma_1 and gamma_2
-        # we call them lambda because otherwise they are renamed when using .from_pretrained
-        gamma_1 = state_dict.pop(f"{prefix}blocks.{i}.gamma_1")
-        gamma_2 = state_dict.pop(f"{prefix}blocks.{i}.gamma_2")
-
-        state_dict[f"beit.encoder.layer.{i}.lambda_1"] = gamma_1
-        state_dict[f"beit.encoder.layer.{i}.lambda_2"] = gamma_2
-
-        # relative_position bias table + index
-        if not has_lm_head:
-            # each layer has its own relative position bias
-            table = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table")
-            index = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index")
-
-            state_dict[
-                f"beit.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table"
-            ] = table
-            state_dict[
-                f"beit.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index"
-            ] = index
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path):
-    """
-    Copy/paste/tweak model's weights to our BEiT structure.
-    """
-
-    # define default BEiT configuration
-    config = BeitConfig()
-    has_lm_head = False
-    is_semantic = False
-    repo_id = "huggingface/label-files"
-    # set config parameters based on URL
-    if checkpoint_url[-9:-4] == "pt22k":
-        # masked image modeling
-        config.use_shared_relative_position_bias = True
-        config.use_mask_token = True
-        has_lm_head = True
-    elif checkpoint_url[-9:-4] == "ft22k":
-        # intermediate fine-tuning on ImageNet-22k
-        config.use_relative_position_bias = True
-        config.num_labels = 21841
-        filename = "imagenet-22k-id2label.json"
-        id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-        id2label = {int(k): v for k, v in id2label.items()}
-        # this dataset contains 21843 labels but the model only has 21841
-        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
-        del id2label[9205]
-        del id2label[15027]
-        config.id2label = id2label
-        config.label2id = {v: k for k, v in id2label.items()}
-    elif checkpoint_url[-8:-4] == "to1k":
-        # fine-tuning on ImageNet-1k
-        config.use_relative_position_bias = True
-        config.num_labels = 1000
-        filename = "imagenet-1k-id2label.json"
-        id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-        id2label = {int(k): v for k, v in id2label.items()}
-        config.id2label = id2label
-        config.label2id = {v: k for k, v in id2label.items()}
-        if "384" in checkpoint_url:
-            config.image_size = 384
-        if "512" in checkpoint_url:
-            config.image_size = 512
-    elif "ade20k" in checkpoint_url:
-        # fine-tuning
-        config.use_relative_position_bias = True
-        config.num_labels = 150
-        filename = "ade20k-id2label.json"
-        id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-        id2label = {int(k): v for k, v in id2label.items()}
-        config.id2label = id2label
-        config.label2id = {v: k for k, v in id2label.items()}
-        config.image_size = 640
-        is_semantic = True
-    else:
-        raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'")
-
-    # size of the architecture
-    if "base" in checkpoint_url:
-        pass
-    elif "large" in checkpoint_url:
-        config.hidden_size = 1024
-        config.intermediate_size = 4096
-        config.num_hidden_layers = 24
-        config.num_attention_heads = 16
-        if "ade20k" in checkpoint_url:
-            config.image_size = 640
-            config.out_indices = [7, 11, 15, 23]
-    else:
-        raise ValueError("Should either find 'base' or 'large' in checkpoint URL")
-
-    # load state_dict of original model, remove and rename some keys
-    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", check_hash=True)
-    state_dict = state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]
-
-    rename_keys = create_rename_keys(config, has_lm_head=has_lm_head, is_semantic=is_semantic)
-    for src, dest in rename_keys:
-        rename_key(state_dict, src, dest)
-    read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head, is_semantic=is_semantic)
-    if is_semantic:
-        # add prefix to decoder keys
-        for key, val in state_dict.copy().items():
-            val = state_dict.pop(key)
-            if key.startswith("backbone.fpn"):
-                key = key.replace("backbone.fpn", "fpn")
-            state_dict[key] = val
-
-    # load HuggingFace model
-    if checkpoint_url[-9:-4] == "pt22k":
-        model = BeitForMaskedImageModeling(config)
-    elif "ade20k" in checkpoint_url:
-        model = BeitForSemanticSegmentation(config)
-    else:
-        model = BeitForImageClassification(config)
-    model.eval()
-    model.load_state_dict(state_dict)
-
-    # Check outputs on an image
-    if is_semantic:
-        image_processor = BeitImageProcessor(size=config.image_size, do_center_crop=False)
-        ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
-        image = Image.open(ds[0]["file"])
-    else:
-        image_processor = BeitImageProcessor(
-            size=config.image_size, resample=PILImageResampling.BILINEAR, do_center_crop=False
-        )
-        image = prepare_img()
-
-    encoding = image_processor(images=image, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-
-    outputs = model(pixel_values)
-    logits = outputs.logits
-
-    # verify logits
-    expected_shape = torch.Size([1, 1000])
-    if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"):
-        expected_shape = torch.Size([1, 196, 8192])
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"):
-        expected_shape = torch.Size([1, 196, 8192])
-    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"):
-        expected_shape = torch.Size([1, 21841])
-        expected_logits = torch.tensor([2.2288, 2.4671, 0.7395])
-        expected_class_idx = 2397
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"):
-        expected_shape = torch.Size([1, 21841])
-        expected_logits = torch.tensor([1.6881, -0.2787, 0.5901])
-        expected_class_idx = 2396
-    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"):
-        expected_logits = torch.tensor([0.1241, 0.0798, -0.6569])
-        expected_class_idx = 285
-    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"):
-        expected_logits = torch.tensor([-1.2385, -1.0987, -1.0108])
-        expected_class_idx = 281
-    elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"):
-        expected_logits = torch.tensor([-1.5303, -0.9484, -0.3147])
-        expected_class_idx = 761
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"):
-        expected_logits = torch.tensor([0.4610, -0.0928, 0.2086])
-        expected_class_idx = 761
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"):
-        expected_logits = torch.tensor([-0.4804, 0.6257, -0.1837])
-        expected_class_idx = 761
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"):
-        expected_logits = torch.tensor([[-0.5122, 0.5117, -0.2113]])
-        expected_class_idx = 761
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"):
-        expected_logits = torch.tensor([-0.3062, 0.7261, 0.4852])
-        expected_class_idx = 761
-    elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"):
-        expected_shape = (1, 150, 160, 160)
-        expected_logits = torch.tensor(
-            [
-                [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
-                [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
-                [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
-            ]
-        )
-    elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"):
-        expected_shape = (1, 150, 160, 160)
-        expected_logits = torch.tensor(
-            [
-                [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
-                [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
-                [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
-            ]
-        )
-    else:
-        raise ValueError("Can't verify logits as model is not supported")
-
-    if logits.shape != expected_shape:
-        raise ValueError(f"Shape of logits not as expected. {logits.shape=}, {expected_shape=}")
-    if not has_lm_head:
-        if is_semantic:
-            if not torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-3):
-                raise ValueError("First elements of logits not as expected")
-        else:
-            print("Predicted class idx:", logits.argmax(-1).item())
-
-            if not torch.allclose(logits[0, :3], expected_logits, atol=1e-3):
-                raise ValueError("First elements of logits not as expected")
-            if logits.argmax(-1).item() != expected_class_idx:
-                raise ValueError("Predicted class index not as expected")
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    print(f"Saving model to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Saving image processor to {pytorch_dump_folder_path}")
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--checkpoint_url",
-        default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
-        type=str,
-        help="URL to the original PyTorch checkpoint (.pth file).",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py

@@ -1,246 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official (now
-deprecated) GitHub: https://github.com/tensorflow/models/tree/v2.3.0/official/nlp/bert
-
-TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert
-weight names to the original names, so the model can be imported with Huggingface/transformer.
-
-You may adapt this script to include classification/MLM/NSP/etc. heads.
-
-Note: This script is only working with an older version of the TensorFlow models repository (<= v2.3.0).
-      Models trained with never versions are not compatible with this script.
-"""
-
-import argparse
-import os
-import re
-
-import tensorflow as tf
-import torch
-
-from transformers import BertConfig, BertModel
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def load_tf2_weights_in_bert(model, tf_checkpoint_path, config):
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    layer_depth = []
-    for full_name, shape in init_vars:
-        # logger.info(f"Loading TF weight {name} with shape {shape}")
-        name = full_name.split("/")
-        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
-            logger.info(f"Skipping non-model layer {full_name}")
-            continue
-        if "optimizer" in full_name:
-            logger.info(f"Skipping optimization layer {full_name}")
-            continue
-        if name[0] == "model":
-            # ignore initial 'model'
-            name = name[1:]
-        # figure out how many levels deep the name is
-        depth = 0
-        for _name in name:
-            if _name.startswith("layer_with_weights"):
-                depth += 1
-            else:
-                break
-        layer_depth.append(depth)
-        # read data
-        array = tf.train.load_variable(tf_path, full_name)
-        names.append("/".join(name))
-        arrays.append(array)
-    logger.info(f"Read a total of {len(arrays):,} layers")
-
-    # Sanity check
-    if len(set(layer_depth)) != 1:
-        raise ValueError(f"Found layer names with different depths (layer depth {list(set(layer_depth))})")
-    layer_depth = list(set(layer_depth))[0]
-    if layer_depth != 1:
-        raise ValueError(
-            "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
-            " heads."
-        )
-
-    # convert layers
-    logger.info("Converting weights...")
-    for full_name, array in zip(names, arrays):
-        name = full_name.split("/")
-        pointer = model
-        trace = []
-        for i, m_name in enumerate(name):
-            if m_name == ".ATTRIBUTES":
-                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
-                break
-            if m_name.startswith("layer_with_weights"):
-                layer_num = int(m_name.split("-")[-1])
-                if layer_num <= 2:
-                    # embedding layers
-                    # layer_num 0: word_embeddings
-                    # layer_num 1: position_embeddings
-                    # layer_num 2: token_type_embeddings
-                    continue
-                elif layer_num == 3:
-                    # embedding LayerNorm
-                    trace.extend(["embeddings", "LayerNorm"])
-                    pointer = getattr(pointer, "embeddings")
-                    pointer = getattr(pointer, "LayerNorm")
-                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
-                    # encoder layers
-                    trace.extend(["encoder", "layer", str(layer_num - 4)])
-                    pointer = getattr(pointer, "encoder")
-                    pointer = getattr(pointer, "layer")
-                    pointer = pointer[layer_num - 4]
-                elif layer_num == config.num_hidden_layers + 4:
-                    # pooler layer
-                    trace.extend(["pooler", "dense"])
-                    pointer = getattr(pointer, "pooler")
-                    pointer = getattr(pointer, "dense")
-            elif m_name == "embeddings":
-                trace.append("embeddings")
-                pointer = getattr(pointer, "embeddings")
-                if layer_num == 0:
-                    trace.append("word_embeddings")
-                    pointer = getattr(pointer, "word_embeddings")
-                elif layer_num == 1:
-                    trace.append("position_embeddings")
-                    pointer = getattr(pointer, "position_embeddings")
-                elif layer_num == 2:
-                    trace.append("token_type_embeddings")
-                    pointer = getattr(pointer, "token_type_embeddings")
-                else:
-                    raise ValueError(f"Unknown embedding layer with name {full_name}")
-                trace.append("weight")
-                pointer = getattr(pointer, "weight")
-            elif m_name == "_attention_layer":
-                # self-attention layer
-                trace.extend(["attention", "self"])
-                pointer = getattr(pointer, "attention")
-                pointer = getattr(pointer, "self")
-            elif m_name == "_attention_layer_norm":
-                # output attention norm
-                trace.extend(["attention", "output", "LayerNorm"])
-                pointer = getattr(pointer, "attention")
-                pointer = getattr(pointer, "output")
-                pointer = getattr(pointer, "LayerNorm")
-            elif m_name == "_attention_output_dense":
-                # output attention dense
-                trace.extend(["attention", "output", "dense"])
-                pointer = getattr(pointer, "attention")
-                pointer = getattr(pointer, "output")
-                pointer = getattr(pointer, "dense")
-            elif m_name == "_output_dense":
-                # output dense
-                trace.extend(["output", "dense"])
-                pointer = getattr(pointer, "output")
-                pointer = getattr(pointer, "dense")
-            elif m_name == "_output_layer_norm":
-                # output dense
-                trace.extend(["output", "LayerNorm"])
-                pointer = getattr(pointer, "output")
-                pointer = getattr(pointer, "LayerNorm")
-            elif m_name == "_key_dense":
-                # attention key
-                trace.append("key")
-                pointer = getattr(pointer, "key")
-            elif m_name == "_query_dense":
-                # attention query
-                trace.append("query")
-                pointer = getattr(pointer, "query")
-            elif m_name == "_value_dense":
-                # attention value
-                trace.append("value")
-                pointer = getattr(pointer, "value")
-            elif m_name == "_intermediate_dense":
-                # attention intermediate dense
-                trace.extend(["intermediate", "dense"])
-                pointer = getattr(pointer, "intermediate")
-                pointer = getattr(pointer, "dense")
-            elif m_name == "_output_layer_norm":
-                # output layer norm
-                trace.append("output")
-                pointer = getattr(pointer, "output")
-            # weights & biases
-            elif m_name in ["bias", "beta"]:
-                trace.append("bias")
-                pointer = getattr(pointer, "bias")
-            elif m_name in ["kernel", "gamma"]:
-                trace.append("weight")
-                pointer = getattr(pointer, "weight")
-            else:
-                logger.warning(f"Ignored {m_name}")
-        # for certain layers reshape is necessary
-        trace = ".".join(trace)
-        if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match(
-            r"(\S+)\.attention\.output\.dense\.weight", trace
-        ):
-            array = array.reshape(pointer.data.shape)
-        if "kernel" in full_name:
-            array = array.transpose()
-        if pointer.shape == array.shape:
-            pointer.data = torch.from_numpy(array)
-        else:
-            raise ValueError(
-                f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"
-                f" {array.shape}"
-            )
-        logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}")
-    return model
-
-
-def convert_tf2_checkpoint_to_pytorch(tf_checkpoint_path, config_path, pytorch_dump_path):
-    # Instantiate model
-    logger.info(f"Loading model based on config from {config_path}...")
-    config = BertConfig.from_json_file(config_path)
-    model = BertModel(config)
-
-    # Load weights from checkpoint
-    logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}...")
-    load_tf2_weights_in_bert(model, tf_checkpoint_path, config)
-
-    # Save pytorch-model
-    logger.info(f"Saving PyTorch model to {pytorch_dump_path}...")
-    torch.save(model.state_dict(), pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
-    )
-    parser.add_argument(
-        "--bert_config_file",
-        type=str,
-        required=True,
-        help="The config json file corresponding to the BERT model. This specifies the model architecture.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_path",
-        type=str,
-        required=True,
-        help="Path to the output PyTorch model (must include filename).",
-    )
-    args = parser.parse_args()
-    convert_tf2_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py

@@ -1,62 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BERT checkpoint."""
-
-import argparse
-
-import torch
-
-from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
-    # Initialise PyTorch model
-    config = BertConfig.from_json_file(bert_config_file)
-    print(f"Building PyTorch model from configuration: {config}")
-    model = BertForPreTraining(config)
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_bert(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    torch.save(model.state_dict(), pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--bert_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained BERT model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py

@@ -1,112 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Convert Huggingface Pytorch checkpoint to Tensorflow checkpoint."""
-
-import argparse
-import os
-
-import numpy as np
-import tensorflow as tf
-import torch
-
-from transformers import BertModel
-
-
-def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name: str):
-    """
-    Args:
-        model: BertModel Pytorch model instance to be converted
-        ckpt_dir: Tensorflow model directory
-        model_name: model name
-
-    Currently supported HF models:
-
-        - Y BertModel
-        - N BertForMaskedLM
-        - N BertForPreTraining
-        - N BertForMultipleChoice
-        - N BertForNextSentencePrediction
-        - N BertForSequenceClassification
-        - N BertForQuestionAnswering
-    """
-
-    tensors_to_transpose = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
-
-    var_map = (
-        ("layer.", "layer_"),
-        ("word_embeddings.weight", "word_embeddings"),
-        ("position_embeddings.weight", "position_embeddings"),
-        ("token_type_embeddings.weight", "token_type_embeddings"),
-        (".", "/"),
-        ("LayerNorm/weight", "LayerNorm/gamma"),
-        ("LayerNorm/bias", "LayerNorm/beta"),
-        ("weight", "kernel"),
-    )
-
-    if not os.path.isdir(ckpt_dir):
-        os.makedirs(ckpt_dir)
-
-    state_dict = model.state_dict()
-
-    def to_tf_var_name(name: str):
-        for patt, repl in iter(var_map):
-            name = name.replace(patt, repl)
-        return f"bert/{name}"
-
-    def create_tf_var(tensor: np.ndarray, name: str, session: tf.Session):
-        tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
-        tf_var = tf.get_variable(dtype=tf_dtype, shape=tensor.shape, name=name, initializer=tf.zeros_initializer())
-        session.run(tf.variables_initializer([tf_var]))
-        session.run(tf_var)
-        return tf_var
-
-    tf.reset_default_graph()
-    with tf.Session() as session:
-        for var_name in state_dict:
-            tf_name = to_tf_var_name(var_name)
-            torch_tensor = state_dict[var_name].numpy()
-            if any(x in var_name for x in tensors_to_transpose):
-                torch_tensor = torch_tensor.T
-            tf_var = create_tf_var(tensor=torch_tensor, name=tf_name, session=session)
-            tf_var.assign(tf.cast(torch_tensor, tf_var.dtype))
-            tf_weight = session.run(tf_var)
-            print(f"Successfully created {tf_name}: {np.allclose(tf_weight, torch_tensor)}")
-
-        saver = tf.train.Saver(tf.trainable_variables())
-        saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
-
-
-def main(raw_args=None):
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_name", type=str, required=True, help="model name e.g. google-bert/bert-base-uncased")
-    parser.add_argument(
-        "--cache_dir", type=str, default=None, required=False, help="Directory containing pytorch model"
-    )
-    parser.add_argument("--pytorch_model_path", type=str, required=True, help="/path/to/<pytorch-model-name>.bin")
-    parser.add_argument("--tf_cache_dir", type=str, required=True, help="Directory in which to save tensorflow model")
-    args = parser.parse_args(raw_args)
-
-    model = BertModel.from_pretrained(
-        pretrained_model_name_or_path=args.model_name,
-        state_dict=torch.load(args.pytorch_model_path),
-        cache_dir=args.cache_dir,
-    )
-
-    convert_pytorch_checkpoint_to_tf(model=model, ckpt_dir=args.tf_cache_dir, model_name=args.model_name)
-
-
-if __name__ == "__main__":
-    main()

src/transformers/models/bert/convert_bert_token_dropping_original_tf2_checkpoint_to_pytorch.py

@@ -1,188 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script converts a lm-head checkpoint from the "Token Dropping" implementation into a PyTorch-compatible BERT
-model. The official implementation of "Token Dropping" can be found in the TensorFlow Models repository:
-
-https://github.com/tensorflow/models/tree/master/official/projects/token_dropping
-"""
-
-import argparse
-
-import tensorflow as tf
-import torch
-
-from transformers import BertConfig, BertForMaskedLM
-from transformers.models.bert.modeling_bert import (
-    BertIntermediate,
-    BertLayer,
-    BertOutput,
-    BertPooler,
-    BertSelfAttention,
-    BertSelfOutput,
-)
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_checkpoint_to_pytorch(tf_checkpoint_path: str, config_path: str, pytorch_dump_path: str):
-    def get_masked_lm_array(name: str):
-        full_name = f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
-        array = tf.train.load_variable(tf_checkpoint_path, full_name)
-
-        if "kernel" in name:
-            array = array.transpose()
-
-        return torch.from_numpy(array)
-
-    def get_encoder_array(name: str):
-        full_name = f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
-        array = tf.train.load_variable(tf_checkpoint_path, full_name)
-
-        if "kernel" in name:
-            array = array.transpose()
-
-        return torch.from_numpy(array)
-
-    def get_encoder_layer_array(layer_index: int, name: str):
-        full_name = f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
-        array = tf.train.load_variable(tf_checkpoint_path, full_name)
-
-        if "kernel" in name:
-            array = array.transpose()
-
-        return torch.from_numpy(array)
-
-    def get_encoder_attention_layer_array(layer_index: int, name: str, orginal_shape):
-        full_name = f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
-        array = tf.train.load_variable(tf_checkpoint_path, full_name)
-        array = array.reshape(orginal_shape)
-
-        if "kernel" in name:
-            array = array.transpose()
-
-        return torch.from_numpy(array)
-
-    print(f"Loading model based on config from {config_path}...")
-    config = BertConfig.from_json_file(config_path)
-    model = BertForMaskedLM(config)
-
-    # Layers
-    for layer_index in range(0, config.num_hidden_layers):
-        layer: BertLayer = model.bert.encoder.layer[layer_index]
-
-        # Self-attention
-        self_attn: BertSelfAttention = layer.attention.self
-
-        self_attn.query.weight.data = get_encoder_attention_layer_array(
-            layer_index, "_query_dense/kernel", self_attn.query.weight.data.shape
-        )
-        self_attn.query.bias.data = get_encoder_attention_layer_array(
-            layer_index, "_query_dense/bias", self_attn.query.bias.data.shape
-        )
-        self_attn.key.weight.data = get_encoder_attention_layer_array(
-            layer_index, "_key_dense/kernel", self_attn.key.weight.data.shape
-        )
-        self_attn.key.bias.data = get_encoder_attention_layer_array(
-            layer_index, "_key_dense/bias", self_attn.key.bias.data.shape
-        )
-        self_attn.value.weight.data = get_encoder_attention_layer_array(
-            layer_index, "_value_dense/kernel", self_attn.value.weight.data.shape
-        )
-        self_attn.value.bias.data = get_encoder_attention_layer_array(
-            layer_index, "_value_dense/bias", self_attn.value.bias.data.shape
-        )
-
-        # Self-attention Output
-        self_output: BertSelfOutput = layer.attention.output
-
-        self_output.dense.weight.data = get_encoder_attention_layer_array(
-            layer_index, "_output_dense/kernel", self_output.dense.weight.data.shape
-        )
-        self_output.dense.bias.data = get_encoder_attention_layer_array(
-            layer_index, "_output_dense/bias", self_output.dense.bias.data.shape
-        )
-
-        self_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/gamma")
-        self_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/beta")
-
-        # Intermediate
-        intermediate: BertIntermediate = layer.intermediate
-
-        intermediate.dense.weight.data = get_encoder_layer_array(layer_index, "_intermediate_dense/kernel")
-        intermediate.dense.bias.data = get_encoder_layer_array(layer_index, "_intermediate_dense/bias")
-
-        # Output
-        bert_output: BertOutput = layer.output
-
-        bert_output.dense.weight.data = get_encoder_layer_array(layer_index, "_output_dense/kernel")
-        bert_output.dense.bias.data = get_encoder_layer_array(layer_index, "_output_dense/bias")
-
-        bert_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_output_layer_norm/gamma")
-        bert_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_output_layer_norm/beta")
-
-    # Embeddings
-    model.bert.embeddings.position_embeddings.weight.data = get_encoder_array("_position_embedding_layer/embeddings")
-    model.bert.embeddings.token_type_embeddings.weight.data = get_encoder_array("_type_embedding_layer/embeddings")
-    model.bert.embeddings.LayerNorm.weight.data = get_encoder_array("_embedding_norm_layer/gamma")
-    model.bert.embeddings.LayerNorm.bias.data = get_encoder_array("_embedding_norm_layer/beta")
-
-    # LM Head
-    lm_head = model.cls.predictions.transform
-
-    lm_head.dense.weight.data = get_masked_lm_array("dense/kernel")
-    lm_head.dense.bias.data = get_masked_lm_array("dense/bias")
-
-    lm_head.LayerNorm.weight.data = get_masked_lm_array("layer_norm/gamma")
-    lm_head.LayerNorm.bias.data = get_masked_lm_array("layer_norm/beta")
-
-    model.bert.embeddings.word_embeddings.weight.data = get_masked_lm_array("embedding_table")
-
-    # Pooling
-    model.bert.pooler = BertPooler(config=config)
-    model.bert.pooler.dense.weight.data: BertPooler = get_encoder_array("_pooler_layer/kernel")
-    model.bert.pooler.dense.bias.data: BertPooler = get_encoder_array("_pooler_layer/bias")
-
-    # Export final model
-    model.save_pretrained(pytorch_dump_path)
-
-    # Integration test - should load without any errors ;)
-    new_model = BertForMaskedLM.from_pretrained(pytorch_dump_path)
-    print(new_model.eval())
-
-    print("Model conversion was done sucessfully!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
-    )
-    parser.add_argument(
-        "--bert_config_file",
-        type=str,
-        required=True,
-        help="The config json file corresponding to the BERT model. This specifies the model architecture.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_path",
-        type=str,
-        required=True,
-        help="Path to the output PyTorch model.",
-    )
-    args = parser.parse_args()
-    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

src/transformers/models/big_bird/convert_bigbird_original_tf_checkpoint_to_pytorch.py

@@ -1,69 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BigBird checkpoint."""
-
-import argparse
-
-from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa):
-    # Initialise PyTorch model
-    config = BigBirdConfig.from_json_file(big_bird_config_file)
-    print(f"Building PyTorch model from configuration: {config}")
-
-    if is_trivia_qa:
-        model = BigBirdForQuestionAnswering(config)
-    else:
-        model = BigBirdForPreTraining(config)
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=is_trivia_qa)
-
-    # Save pytorch-model
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    model.save_pretrained(pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--big_bird_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained BERT model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    parser.add_argument(
-        "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
-    )
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(
-        args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
-    )

src/transformers/models/bigbird_pegasus/convert_bigbird_pegasus_tf_to_pytorch.py

@@ -1,170 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-from typing import Dict
-
-import tensorflow as tf
-import torch
-from tqdm import tqdm
-
-from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
-
-
-INIT_COMMON = [
-    # tf -> hf
-    ("/", "."),
-    ("layer_", "layers."),
-    ("kernel", "weight"),
-    ("beta", "bias"),
-    ("gamma", "weight"),
-    ("pegasus", "model"),
-]
-END_COMMON = [
-    (".output.dense", ".fc2"),
-    ("intermediate.LayerNorm", "final_layer_norm"),
-    ("intermediate.dense", "fc1"),
-]
-
-DECODER_PATTERNS = (
-    INIT_COMMON
-    + [
-        ("attention.self.LayerNorm", "self_attn_layer_norm"),
-        ("attention.output.dense", "self_attn.out_proj"),
-        ("attention.self", "self_attn"),
-        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
-        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
-        ("attention.encdec", "encoder_attn"),
-        ("key", "k_proj"),
-        ("value", "v_proj"),
-        ("query", "q_proj"),
-        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
-    ]
-    + END_COMMON
-)
-
-REMAINING_PATTERNS = (
-    INIT_COMMON
-    + [
-        ("embeddings.word_embeddings", "shared.weight"),
-        ("embeddings.position_embeddings", "embed_positions.weight"),
-        ("attention.self.LayerNorm", "self_attn_layer_norm"),
-        ("attention.output.dense", "self_attn.output"),
-        ("attention.self", "self_attn.self"),
-        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
-    ]
-    + END_COMMON
-)
-
-KEYS_TO_IGNORE = [
-    "encdec/key/bias",
-    "encdec/query/bias",
-    "encdec/value/bias",
-    "self/key/bias",
-    "self/query/bias",
-    "self/value/bias",
-    "encdec_output/dense/bias",
-    "attention/output/dense/bias",
-]
-
-
-def rename_state_dict_key(k, patterns):
-    for tf_name, hf_name in patterns:
-        k = k.replace(tf_name, hf_name)
-    return k
-
-
-def convert_bigbird_pegasus(tf_weights: dict, config_update: dict) -> BigBirdPegasusForConditionalGeneration:
-    cfg = BigBirdPegasusConfig(**config_update)
-    torch_model = BigBirdPegasusForConditionalGeneration(cfg)
-    state_dict = torch_model.state_dict()
-    mapping = {}
-
-    # separating decoder weights
-    decoder_weights = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder")}
-    remaining_weights = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder")}
-
-    for k, v in tqdm(decoder_weights.items(), "tf -> hf conversion"):
-        conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
-        if any(conditions):
-            continue
-        patterns = DECODER_PATTERNS
-        new_k = rename_state_dict_key(k, patterns)
-        if new_k not in state_dict:
-            raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
-        if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
-            v = v.T
-        mapping[new_k] = torch.from_numpy(v)
-        assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
-
-    for k, v in tqdm(remaining_weights.items(), "tf -> hf conversion"):
-        conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
-        if any(conditions):
-            continue
-        patterns = REMAINING_PATTERNS
-        new_k = rename_state_dict_key(k, patterns)
-        if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
-            raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
-        if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
-            v = v.T
-        mapping[new_k] = torch.from_numpy(v)
-        if k != "pegasus/embeddings/position_embeddings":
-            assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
-
-    mapping["model.encoder.embed_positions.weight"] = mapping["model.embed_positions.weight"]
-    mapping["model.decoder.embed_positions.weight"] = mapping.pop("model.embed_positions.weight")
-    missing, extra = torch_model.load_state_dict(mapping, strict=False)
-    unexpected_missing = [
-        k
-        for k in missing
-        if k
-        not in [
-            "final_logits_bias",
-            "model.encoder.embed_tokens.weight",
-            "model.decoder.embed_tokens.weight",
-            "lm_head.weight",
-        ]
-    ]
-    assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
-    assert extra == [], f"no matches found for the following tf keys {extra}"
-    return torch_model
-
-
-def get_tf_weights_as_numpy(path) -> Dict:
-    init_vars = tf.train.list_variables(path)
-    tf_weights = {}
-    ignore_name = ["global_step"]
-    for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
-        skip_key = any(pat in name for pat in ignore_name)
-        if skip_key:
-            continue
-        array = tf.train.load_variable(path, name)
-        tf_weights[name] = array
-    return tf_weights
-
-
-def convert_bigbird_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str, config_update: dict):
-    tf_weights = get_tf_weights_as_numpy(ckpt_path)
-    torch_model = convert_bigbird_pegasus(tf_weights, config_update)
-    torch_model.save_pretrained(save_dir)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
-    parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
-    args = parser.parse_args()
-    config_update = {}
-    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)

src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py

@@ -1,292 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import argparse
-import json
-import os
-import re
-import shutil
-
-import torch
-
-from transformers import BioGptConfig, BioGptForCausalLM
-from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
-from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
-from transformers.utils import WEIGHTS_NAME, logging
-
-
-logging.set_verbosity_warning()
-
-json_indent = 2
-
-
-# modified from https://github.com/facebookresearch/fairseq/blob/dd74992d0d143155998e9ed4076826bcea80fb06/fairseq/data/dictionary.py#L18
-class Dictionary:
-    """A mapping from symbols to consecutive integers"""
-
-    def __init__(
-        self,
-        *,  # begin keyword-only arguments
-        bos="<s>",
-        pad="<pad>",
-        eos="</s>",
-        unk="<unk>",
-        extra_special_symbols=None,
-    ):
-        self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos
-        self.symbols = []
-        self.count = []
-        self.indices = {}
-        self.bos_index = self.add_symbol(bos)
-        self.pad_index = self.add_symbol(pad)
-        self.eos_index = self.add_symbol(eos)
-        self.unk_index = self.add_symbol(unk)
-        if extra_special_symbols:
-            for s in extra_special_symbols:
-                self.add_symbol(s)
-        self.nspecial = len(self.symbols)
-
-    def __eq__(self, other):
-        return self.indices == other.indices
-
-    def __getitem__(self, idx):
-        if idx < len(self.symbols):
-            return self.symbols[idx]
-        return self.unk_word
-
-    def __len__(self):
-        """Returns the number of symbols in the dictionary"""
-        return len(self.symbols)
-
-    def __contains__(self, sym):
-        return sym in self.indices
-
-    @classmethod
-    def load(cls, f):
-        """Loads the dictionary from a text file with the format:
-
-        ```
-        <symbol0> <count0>
-        <symbol1> <count1>
-        ...
-        ```
-        """
-        d = cls()
-        d.add_from_file(f)
-        return d
-
-    def add_symbol(self, word, n=1, overwrite=False):
-        """Adds a word to the dictionary"""
-        if word in self.indices and not overwrite:
-            idx = self.indices[word]
-            self.count[idx] = self.count[idx] + n
-            return idx
-        else:
-            idx = len(self.symbols)
-            self.indices[word] = idx
-            self.symbols.append(word)
-            self.count.append(n)
-            return idx
-
-    def _load_meta(self, lines):
-        return 0
-
-    def add_from_file(self, f):
-        """
-        Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
-        """
-        if isinstance(f, str):
-            try:
-                with open(f, "r", encoding="utf-8") as fd:
-                    self.add_from_file(fd)
-            except FileNotFoundError as fnfe:
-                raise fnfe
-            except UnicodeError:
-                raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(f))
-            return
-
-        lines = f.readlines()
-        indices_start_line = self._load_meta(lines)
-
-        for line in lines[indices_start_line:]:
-            try:
-                line, field = line.rstrip().rsplit(" ", 1)
-                if field == "#fairseq:overwrite":
-                    overwrite = True
-                    line, field = line.rsplit(" ", 1)
-                else:
-                    overwrite = False
-                count = int(field)
-                word = line
-                if word in self and not overwrite:
-                    raise RuntimeError(
-                        "Duplicate word found when loading Dictionary: '{}'. "
-                        "Duplicate words can overwrite earlier ones by adding the "
-                        "#fairseq:overwrite flag at the end of the corresponding row "
-                        "in the dictionary file. If using the Camembert model, please "
-                        "download an updated copy of the model file.".format(word)
-                    )
-                self.add_symbol(word, n=count, overwrite=overwrite)
-            except ValueError:
-                raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'")
-
-
-def rewrite_dict_keys(d):
-    # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
-    # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
-    d2 = dict((re.sub(r"@@$", "", k), v) if k.endswith("@@") else (re.sub(r"$", "</w>", k), v) for k, v in d.items())
-    keep_keys = "<s> <pad> </s> <unk>".split()
-    # restore the special tokens
-    for k in keep_keys:
-        del d2[f"{k}</w>"]
-        d2[k] = d[k]  # restore
-    return d2
-
-
-def convert_biogpt_checkpoint_to_pytorch(biogpt_checkpoint_path, pytorch_dump_folder_path):
-    # prep
-    if not os.path.exists(biogpt_checkpoint_path):
-        raise ValueError(f"path {biogpt_checkpoint_path} does not exist!")
-    os.makedirs(pytorch_dump_folder_path, exist_ok=True)
-    print(f"Writing results to {pytorch_dump_folder_path}")
-
-    # handle various types of models
-
-    checkpoint_file = os.path.join(biogpt_checkpoint_path, "checkpoint.pt")
-    if not os.path.isfile(checkpoint_file):
-        raise ValueError(f"path to the file {checkpoint_file} does not exist!")
-    chkpt = torch.load(checkpoint_file, map_location="cpu")
-
-    args = chkpt["cfg"]["model"]
-
-    # dicts
-    dict_file = os.path.join(biogpt_checkpoint_path, "dict.txt")
-    if not os.path.isfile(dict_file):
-        raise ValueError(f"path to the file {dict_file} does not exist!")
-    src_dict = Dictionary.load(dict_file)
-    src_vocab = rewrite_dict_keys(src_dict.indices)
-    src_vocab_size = len(src_vocab)
-    src_vocab_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["vocab_file"])
-    print(f"Generating {src_vocab_file} of {src_vocab_size} records")
-    with open(src_vocab_file, "w", encoding="utf-8") as f:
-        f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))
-
-    # merges_file (bpecodes)
-    bpecodes_file = os.path.join(biogpt_checkpoint_path, "bpecodes")
-    if not os.path.isfile(bpecodes_file):
-        raise ValueError(f"path to the file {bpecodes_file} does not exist!")
-
-    merges_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["merges_file"])
-    shutil.copyfile(bpecodes_file, merges_file)
-
-    # model config
-    biogpt_model_config_file = os.path.join(pytorch_dump_folder_path, "config.json")
-
-    model_conf = {
-        "activation_dropout": args["activation_dropout"],
-        "architectures": ["BioGptForCausalLM"],
-        "attention_probs_dropout_prob": args["attention_dropout"],
-        "bos_token_id": 0,
-        "eos_token_id": 2,
-        "hidden_act": args["activation_fn"],
-        "hidden_dropout_prob": args["dropout"],
-        "hidden_size": args["decoder_embed_dim"],
-        "initializer_range": 0.02,
-        "intermediate_size": args["decoder_ffn_embed_dim"],
-        "layer_norm_eps": 1e-12,
-        "layerdrop": args["decoder_layerdrop"],
-        "max_position_embeddings": args["max_target_positions"],
-        "model_type": "biogpt",
-        "num_attention_heads": args["decoder_attention_heads"],
-        "num_hidden_layers": args["decoder_layers"],
-        "pad_token_id": 1,
-        "scale_embedding": not args["no_scale_embedding"],
-        "tie_word_embeddings": args["share_decoder_input_output_embed"],
-        "vocab_size": src_vocab_size,
-    }
-
-    # good hparam defaults to start with
-
-    print(f"Generating {biogpt_model_config_file}")
-    with open(biogpt_model_config_file, "w", encoding="utf-8") as f:
-        f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))
-
-    # tokenizer config
-    biogpt_tokenizer_config_file = os.path.join(pytorch_dump_folder_path, TOKENIZER_CONFIG_FILE)
-
-    tokenizer_conf = {
-        "bos_token": "<s>",
-        "eos_token": "</s>",
-        "model_max_length": 1024,
-        "pad_token": "<pad>",
-        "special_tokens_map_file": None,
-        "tokenizer_class": "BioGptTokenizer",
-        "unk_token": "<unk>",
-    }
-
-    print(f"Generating {biogpt_tokenizer_config_file}")
-    with open(biogpt_tokenizer_config_file, "w", encoding="utf-8") as f:
-        f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))
-
-    # model
-    model_state_dict = chkpt["model"]
-
-    # remove unneeded keys
-    ignore_keys = [
-        "decoder.version",
-    ]
-    for k in ignore_keys:
-        model_state_dict.pop(k, None)
-
-    layer_names = list(model_state_dict.keys())
-    for layer_name in layer_names:
-        if layer_name.endswith("output_projection.weight"):
-            model_state_dict[layer_name.replace("decoder.", "")] = model_state_dict.pop(layer_name)
-        else:
-            model_state_dict[layer_name.replace("decoder", "biogpt")] = model_state_dict.pop(layer_name)
-
-    config = BioGptConfig.from_pretrained(pytorch_dump_folder_path)
-    model_new = BioGptForCausalLM(config)
-
-    # check that it loads ok
-    model_new.load_state_dict(model_state_dict)
-
-    # save
-    pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
-    print(f"Generating {pytorch_weights_dump_path}")
-    torch.save(model_state_dict, pytorch_weights_dump_path)
-
-    print("Conversion is done!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--biogpt_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
-            " bpecodes, etc."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

src/transformers/models/bit/convert_bit_to_pytorch.py

@@ -1,177 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BiT checkpoints from the timm library."""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-from timm import create_model
-from timm.data import resolve_data_config
-from timm.data.transforms_factory import create_transform
-
-from transformers import BitConfig, BitForImageClassification, BitImageProcessor
-from transformers.image_utils import PILImageResampling
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_config(model_name):
-    repo_id = "huggingface/label-files"
-    filename = "imagenet-1k-id2label.json"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-    label2id = {v: k for k, v in id2label.items()}
-
-    conv_layer = "std_conv" if "bit" in model_name else False
-
-    # note that when using BiT as backbone for ViT-hybrid checkpoints,
-    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
-    # config.conv_layer = "std_conv_same"
-    config = BitConfig(
-        conv_layer=conv_layer,
-        num_labels=1000,
-        id2label=id2label,
-        label2id=label2id,
-    )
-
-    return config
-
-
-def rename_key(name):
-    if "stem.conv" in name:
-        name = name.replace("stem.conv", "bit.embedder.convolution")
-    if "blocks" in name:
-        name = name.replace("blocks", "layers")
-    if "head.fc" in name:
-        name = name.replace("head.fc", "classifier.1")
-    if name.startswith("norm"):
-        name = "bit." + name
-    if "bit" not in name and "classifier" not in name:
-        name = "bit.encoder." + name
-
-    return name
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_bit_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
-    """
-    Copy/paste/tweak model's weights to our BiT structure.
-    """
-
-    # define default BiT configuration
-    config = get_config(model_name)
-
-    # load original model from timm
-    timm_model = create_model(model_name, pretrained=True)
-    timm_model.eval()
-
-    # load state_dict of original model
-    state_dict = timm_model.state_dict()
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        state_dict[rename_key(key)] = val.squeeze() if "head" in key else val
-
-    # load HuggingFace model
-    model = BitForImageClassification(config)
-    model.eval()
-    model.load_state_dict(state_dict)
-
-    # create image processor
-    transform = create_transform(**resolve_data_config({}, model=timm_model))
-    timm_transforms = transform.transforms
-
-    pillow_resamplings = {
-        "bilinear": PILImageResampling.BILINEAR,
-        "bicubic": PILImageResampling.BICUBIC,
-        "nearest": PILImageResampling.NEAREST,
-    }
-
-    processor = BitImageProcessor(
-        do_resize=True,
-        size={"shortest_edge": timm_transforms[0].size},
-        resample=pillow_resamplings[timm_transforms[0].interpolation.value],
-        do_center_crop=True,
-        crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]},
-        do_normalize=True,
-        image_mean=timm_transforms[-1].mean.tolist(),
-        image_std=timm_transforms[-1].std.tolist(),
-    )
-
-    image = prepare_img()
-    timm_pixel_values = transform(image).unsqueeze(0)
-    pixel_values = processor(image, return_tensors="pt").pixel_values
-
-    # verify pixel values
-    assert torch.allclose(timm_pixel_values, pixel_values)
-
-    # verify logits
-    with torch.no_grad():
-        outputs = model(pixel_values)
-        logits = outputs.logits
-
-    print("Logits:", logits[0, :3])
-    print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
-    timm_logits = timm_model(pixel_values)
-    assert timm_logits.shape == outputs.logits.shape
-    assert torch.allclose(timm_logits, outputs.logits, atol=1e-3)
-    print("Looks ok!")
-
-    if pytorch_dump_folder_path is not None:
-        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}")
-        model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print(f"Pushing model {model_name} and processor to the hub")
-        model.push_to_hub(f"ybelkada/{model_name}")
-        processor.push_to_hub(f"ybelkada/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="resnetv2_50x1_bitm",
-        type=str,
-        help="Name of the BiT timm model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        action="store_true",
-        help="Whether to push the model to the hub.",
-    )
-
-    args = parser.parse_args()
-    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/blenderbot/convert_blenderbot_original_pytorch_checkpoint_to_pytorch.py

@@ -1,114 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Blenderbot checkpoint."""
-
-import argparse
-
-import torch
-
-from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-PATTERNS = [
-    ["attention", "attn"],
-    ["encoder_attention", "encoder_attn"],
-    ["q_lin", "q_proj"],
-    ["k_lin", "k_proj"],
-    ["v_lin", "v_proj"],
-    ["out_lin", "out_proj"],
-    ["norm_embeddings", "layernorm_embedding"],
-    ["position_embeddings", "embed_positions"],
-    ["embeddings", "embed_tokens"],
-    ["ffn.lin", "fc"],
-]
-
-
-def rename_state_dict_key(k):
-    if k == "embeddings.weight":
-        return "shared.weight"
-
-    for parlai_name, hf_name in PATTERNS:
-        k = k.replace(parlai_name, hf_name)
-
-    if k.startswith("encoder"):
-        k = k.replace(".attn", ".self_attn")
-        k = k.replace("norm1", "self_attn_layer_norm")
-        k = k.replace("norm2", "final_layer_norm")
-    elif k.startswith("decoder"):
-        k = k.replace("norm1", "self_attn_layer_norm")
-        k = k.replace("norm2", "encoder_attn_layer_norm")
-        k = k.replace("norm3", "final_layer_norm")
-    return k
-
-
-def rename_layernorm_keys(sd):
-    keys = [
-        "model.encoder.layernorm_embedding.weight",
-        "model.encoder.layernorm_embedding.bias",
-        "model.decoder.layernorm_embedding.weight",
-        "model.decoder.layernorm_embedding.bias",
-    ]
-    for k in keys:
-        v = sd.pop(k)
-        new_k = k.replace("layernorm_embedding", "layer_norm")
-        assert new_k not in sd
-        sd[new_k] = v
-
-
-IGNORE_KEYS = ["START"]
-
-
-@torch.no_grad()
-def convert_parlai_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_json_path):
-    """
-    Copy/paste/tweak model's weights to our BERT structure.
-    """
-    model = torch.load(checkpoint_path, map_location="cpu")
-    sd = model["model"]
-    cfg = BlenderbotConfig.from_json_file(config_json_path)
-    m = BlenderbotForConditionalGeneration(cfg)
-    valid_keys = m.model.state_dict().keys()
-    failures = []
-    mapping = {}
-    for k, v in sd.items():
-        if k in IGNORE_KEYS:
-            continue
-
-        new_k = rename_state_dict_key(k)
-        if new_k not in valid_keys:
-            failures.append([k, new_k])
-        else:
-            mapping[new_k] = v
-    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
-        rename_layernorm_keys(sd)
-    m.model.load_state_dict(mapping, strict=True)
-    m.half()
-    m.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
-    parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
-    parser.add_argument(
-        "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
-    )
-    args = parser.parse_args()
-    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

src/transformers/models/blip/convert_blip_original_pytorch_to_hf.py

@@ -1,191 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import re
-
-import requests
-import torch
-
-# git clone https://github.com/salesforce/BLIP.git
-from models.blip import blip_decoder
-from models.blip_itm import blip_itm
-from models.blip_vqa import blip_vqa
-from PIL import Image
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-
-from transformers import (
-    BertTokenizer,
-    BlipConfig,
-    BlipForConditionalGeneration,
-    BlipForImageTextRetrieval,
-    BlipForQuestionAnswering,
-)
-
-
-def load_demo_image(image_size, device):
-    img_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
-    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
-
-    transform = transforms.Compose(
-        [
-            transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),
-            transforms.ToTensor(),
-            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
-        ]
-    )
-    image = transform(raw_image).unsqueeze(0).to(device)
-    return image
-
-
-def rename_key(key):
-    if "visual_encoder" in key:
-        key = re.sub("visual_encoder*", "vision_model.encoder", key)
-    if "blocks" in key:
-        key = re.sub(r"blocks", "layers", key)
-    if "attn" in key:
-        key = re.sub(r"attn", "self_attn", key)
-    if "norm1" in key:
-        key = re.sub(r"norm1", "layer_norm1", key)
-    if "norm2" in key:
-        key = re.sub(r"norm2", "layer_norm2", key)
-    if "encoder.norm" in key:
-        key = re.sub(r"encoder.norm", "post_layernorm", key)
-    if "encoder.patch_embed.proj" in key:
-        key = re.sub(r"encoder.patch_embed.proj", "embeddings.patch_embedding", key)
-
-    if "encoder.pos_embed" in key:
-        key = re.sub(r"encoder.pos_embed", "embeddings.position_embedding", key)
-    if "encoder.cls_token" in key:
-        key = re.sub(r"encoder.cls_token", "embeddings.class_embedding", key)
-
-    if "self_attn" in key:
-        key = re.sub(r"self_attn.proj", "self_attn.projection", key)
-
-    return key
-
-
-@torch.no_grad()
-def convert_blip_checkpoint(pytorch_dump_folder_path, config_path=None):
-    """
-    Copy/paste/tweak model's weights to transformers design.
-    """
-    if config_path is not None:
-        config = BlipConfig.from_pretrained(config_path)
-    else:
-        config = BlipConfig(projection_dim=512, text_config={}, vision_config={})
-
-    hf_model = BlipForConditionalGeneration(config).eval()
-
-    model_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"
-
-    pt_model = blip_decoder(pretrained=model_url, image_size=384, vit="base")
-    pt_model = pt_model.eval()
-
-    modified_state_dict = pt_model.state_dict()
-    for key in modified_state_dict.copy():
-        value = modified_state_dict.pop(key)
-        renamed_key = rename_key(key)
-        modified_state_dict[renamed_key] = value
-
-    hf_model.load_state_dict(modified_state_dict)
-
-    image_size = 384
-    image = load_demo_image(image_size=image_size, device="cpu")
-    tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
-    input_ids = tokenizer(["a picture of"]).input_ids
-
-    out = hf_model.generate(image, input_ids)
-
-    assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
-
-    out = hf_model.generate(image)
-
-    assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
-
-    if pytorch_dump_folder_path is not None:
-        hf_model.save_pretrained(pytorch_dump_folder_path)
-
-    # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
-    model_url = (
-        "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
-    )
-
-    vqa_model = blip_vqa(pretrained=model_url, image_size=image_size, vit="base")
-    vqa_model.eval()
-
-    modified_state_dict = vqa_model.state_dict()
-    for key in modified_state_dict.copy():
-        value = modified_state_dict.pop(key)
-        renamed_key = rename_key(key)
-        modified_state_dict[renamed_key] = value
-
-    hf_vqa_model = BlipForQuestionAnswering(config)
-
-    hf_vqa_model.load_state_dict(modified_state_dict)
-
-    question = ["How many dogs are in this image?"]
-    question_input_ids = tokenizer(question, return_tensors="pt").input_ids
-
-    answer = hf_vqa_model.generate(question_input_ids, image)
-    print(tokenizer.decode(answer[0]))
-
-    assert tokenizer.decode(answer[0]) == "[UNK] 1 [SEP]"
-    if pytorch_dump_folder_path is not None:
-        hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa")
-
-    model_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"
-
-    itm_model = blip_itm(pretrained=model_url, image_size=image_size, vit="base")
-    itm_model.eval()
-
-    modified_state_dict = itm_model.state_dict()
-    for key in modified_state_dict.copy():
-        value = modified_state_dict.pop(key)
-        renamed_key = rename_key(key)
-        modified_state_dict[renamed_key] = value
-
-    hf_itm_model = BlipForImageTextRetrieval(config)
-
-    question = ["A picture of a woman with a dog sitting in a beach"]
-    question_input_ids = tokenizer(
-        question,
-        return_tensors="pt",
-        padding="max_length",
-        truncation=True,
-        max_length=35,
-    ).input_ids
-
-    hf_itm_model.load_state_dict(modified_state_dict)
-    hf_itm_model.eval()
-
-    out_itm = hf_itm_model(question_input_ids, image, use_itm_head=True)
-    out = hf_itm_model(question_input_ids, image, use_itm_head=False)
-
-    assert out[0].item() == 0.2110687494277954
-    assert torch.nn.functional.softmax(out_itm[0], dim=1)[:, 1].item() == 0.45698845386505127
-
-    if pytorch_dump_folder_path is not None:
-        hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
-    args = parser.parse_args()
-
-    convert_blip_checkpoint(args.pytorch_dump_folder_path, args.config_path)

src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py

@@ -1,390 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Convert BLIP-2 checkpoints from the original repository.
-
-URL: https://github.com/salesforce/LAVIS/tree/main/projects/blip2
-"""
-
-import argparse
-
-import requests
-import torch
-
-# pip3 install salesforce-lavis
-# I'm actually installing a slightly modified version: pip3 install -U git+https://github.com/nielsrogge/LAVIS.git@blip2_float32
-# to make sure we can compare both original and HF implementation in float32
-from lavis.models import load_model_and_preprocess
-from PIL import Image
-
-from transformers import (
-    AutoTokenizer,
-    BertTokenizer,
-    Blip2Config,
-    Blip2ForConditionalGeneration,
-    Blip2ForImageTextRetrieval,
-    Blip2Processor,
-    Blip2QFormerConfig,
-    Blip2VisionConfig,
-    BlipImageProcessor,
-    OPTConfig,
-    T5Config,
-    set_seed,
-)
-from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
-
-
-def load_demo_image():
-    url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
-    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
-
-    return image
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def create_rename_keys(config, model_name):
-    rename_keys = []
-    # fmt: off
-
-    # vision encoder
-    rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding"))
-    rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding"))
-    rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight"))
-    rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias"))
-    rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight"))
-    rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias"))
-
-    for i in range(config.vision_config.num_hidden_layers):
-        rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",))
-        rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
-        rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
-
-    # QFormer
-    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight"))
-    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias"))
-    if "itm" in model_name:
-        rename_keys.append(("Qformer.bert.embeddings.word_embeddings.weight", "embeddings.word_embeddings.weight"))
-        rename_keys.append(("Qformer.bert.embeddings.position_embeddings.weight", "embeddings.position_embeddings.weight"))
-        rename_keys.append(("vision_proj.weight", "vision_projection.weight"))
-        rename_keys.append(("vision_proj.bias", "vision_projection.bias"))
-        rename_keys.append(("text_proj.weight", "text_projection.weight"))
-        rename_keys.append(("text_proj.bias", "text_projection.bias"))
-
-    # fmt: on
-    return rename_keys
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-def read_in_q_v_bias(state_dict, config):
-    for i in range(config.vision_config.num_hidden_layers):
-        # read in original q and v biases
-        q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
-        v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")
-
-        # next, set bias in the state dict
-        qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
-        state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias
-
-
-def get_blip2_config(model_name, eos_token_id):
-    image_size = 364 if "coco" in model_name else 224
-    vision_config = Blip2VisionConfig(image_size=image_size).to_dict()
-
-    # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
-    # seems like flan-T5 models don't have bos_token_id properly set?
-    if "opt-2.7b" in model_name:
-        text_config = OPTConfig.from_pretrained("facebook/opt-2.7b", eos_token_id=eos_token_id).to_dict()
-    elif "opt-6.7b" in model_name:
-        text_config = OPTConfig.from_pretrained("facebook/opt-6.7b", eos_token_id=eos_token_id).to_dict()
-    elif "t5-xl" in model_name:
-        text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict()
-    elif "t5-xxl" in model_name:
-        text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict()
-    elif "itm" in model_name:
-        text_config = {}
-    else:
-        raise ValueError("Model name not supported")
-
-    if "itm" in model_name:
-        config = Blip2Config(
-            vision_config=vision_config,
-            qformer_config=Blip2QFormerConfig(vocab_size=30523, use_qformer_text_input=True).to_dict(),
-        )
-    else:
-        config = Blip2Config(vision_config=vision_config, text_config=text_config)
-
-    return config, image_size
-
-
-@torch.no_grad()
-def convert_blip2_checkpoint(
-    model_name, pytorch_dump_folder_path=None, push_to_hub=False, lavis_device="cpu", hf_model_device="cpu"
-):
-    """
-    Copy/paste/tweak model's weights to Transformers design.
-    """
-    if "opt" in model_name:
-        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-2.7b")
-    elif "itm" in model_name:
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", truncation_side="right")
-        tokenizer.add_special_tokens({"bos_token": "[DEC]"})
-    else:
-        tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
-
-    if "itm" in model_name:
-        eos_token_id = None
-    else:
-        eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0]
-    config, image_size = get_blip2_config(model_name, eos_token_id=eos_token_id)
-
-    if "itm" in model_name:
-        hf_model = Blip2ForImageTextRetrieval(config).eval()
-    else:
-        hf_model = Blip2ForConditionalGeneration(config).eval()
-
-    model_name_to_original = {
-        "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
-        "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
-        "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
-        "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
-        "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
-        "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
-        "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
-        "blip2-itm-vit-g": ("blip2_image_text_matching", "pretrain"),
-        "blip2-itm-vit-g-coco": ("blip2_image_text_matching", "coco"),
-    }
-
-    name, type = model_name_to_original[model_name]
-
-    # load original model
-    print("Loading original model...")
-    original_model, vis_processors, _ = load_model_and_preprocess(
-        name=name, model_type=type, is_eval=True, device=lavis_device
-    )
-    original_model.eval()
-    print("Done!")
-
-    # update state dict keys
-    state_dict = original_model.state_dict()
-    rename_keys = create_rename_keys(config, model_name)
-    for src, dest in rename_keys:
-        rename_key(state_dict, src, dest)
-
-    # some keys can be renamed efficiently
-    for key, val in state_dict.copy().items():
-        val = state_dict.pop(key)
-        if key.startswith("Qformer.bert"):
-            key = key.replace("Qformer.bert", "qformer")
-        if "attention.self" in key:
-            key = key.replace("self", "attention")
-        if "opt_proj" in key:
-            key = key.replace("opt_proj", "language_projection")
-        if "t5_proj" in key:
-            key = key.replace("t5_proj", "language_projection")
-        if key.startswith("opt"):
-            key = key.replace("opt", "language")
-        if key.startswith("t5"):
-            key = key.replace("t5", "language")
-        state_dict[key] = val
-
-    # read in qv biases
-    read_in_q_v_bias(state_dict, config)
-
-    missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False)
-    assert len(missing_keys) == 0
-
-    if "itm" in model_name:
-        unexpected_keys = list(filter(lambda x: not x.startswith("Qformer.cls"), unexpected_keys))
-        assert unexpected_keys == ["temp", "qformer.embeddings.position_ids"]
-    else:
-        assert unexpected_keys == ["qformer.embeddings.position_ids"]
-
-    image = load_demo_image()
-    original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(lavis_device)
-
-    # create processor
-    image_processor = BlipImageProcessor(
-        size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD
-    )
-    processor = Blip2Processor(image_processor=image_processor, tokenizer=tokenizer)
-    pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(hf_model_device)
-
-    # make sure processor creates exact same pixel values
-    assert torch.allclose(pixel_values, original_pixel_values.to(pixel_values.device))
-
-    original_model.to(lavis_device)
-    hf_model.to(hf_model_device)
-
-    if "itm" in model_name:
-        caption = "a large fountain spewing water into the air"
-        input_ids = tokenizer([caption], return_tensors="pt").input_ids.to(hf_model_device)
-        attention_mask = processor(text=caption, return_tensors="pt").attention_mask.to(hf_model_device)
-
-        with torch.no_grad():
-            original_logits = original_model(
-                {"image": original_pixel_values, "text_input": [caption]}, match_head="itm"
-            )
-            logits = hf_model(
-                pixel_values=pixel_values,
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                use_image_text_matching_head=True,
-            )
-
-        assert original_logits.shape == logits.logits_per_image.shape
-        print("First values of original logits:", original_logits[0, :3])
-        print("First values of HF logits:", logits.logits_per_image[0, :3])
-
-        # assert values
-        # cast to same type
-        target_dtype = logits.logits_per_image.dtype
-        assert torch.allclose(original_logits.to(target_dtype), logits.logits_per_image, atol=1e-4)
-
-        original_itm_scores = torch.nn.functional.softmax(original_logits, dim=1)
-        itm_scores = torch.nn.functional.softmax(logits.logits_per_image, dim=1)
-        assert torch.allclose(original_itm_scores.to(target_dtype), itm_scores, atol=1e-4)
-        print("Looks ok!")
-
-        with torch.no_grad():
-            original_logits = original_model(
-                {"image": original_pixel_values, "text_input": [caption]}, match_head="itc"
-            )
-            logits = hf_model(
-                pixel_values=pixel_values,
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                use_image_text_matching_head=False,
-            )
-
-        assert original_logits.shape == logits.logits_per_image.shape
-        print("First values of original logits:", original_logits[0, :3])
-        print("First values of HF logits:", logits.logits_per_image[0, :3])
-
-        # assert values
-        # cast to same type
-        target_dtype = logits.logits_per_image.dtype
-        assert torch.allclose(original_logits.to(target_dtype), logits.logits_per_image, atol=1e-4)
-        print("Looks ok!")
-
-    else:
-        input_ids = tokenizer(["\n"], return_tensors="pt").input_ids.to(hf_model_device)
-
-        with torch.no_grad():
-            if "opt" in model_name:
-                original_logits = original_model({"image": original_pixel_values, "text_input": [""]}).logits
-                logits = hf_model(pixel_values, input_ids).logits
-            else:
-                original_logits = original_model(
-                    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}
-                ).logits
-                labels = input_ids.masked_fill(input_ids == tokenizer.pad_token_id, -100)
-                logits = hf_model(pixel_values, input_ids, labels=labels).logits
-
-        assert original_logits.shape == logits.shape
-        print("First values of original logits:", original_logits[0, :3, :3])
-        print("First values of HF logits:", logits[0, :3, :3])
-
-        # assert values
-        assert torch.allclose(original_logits.to(logits.device), logits, atol=1e-4)
-        print("Looks ok!")
-
-        print("Generating a caption...")
-        prompt = "Question: what object is in this image? Answer:"
-        input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(hf_model_device)
-
-        set_seed(42)
-
-        original_outputs = original_model.generate(
-            {"image": original_pixel_values, "prompt": prompt}, use_nucleus_sampling=True, max_length=50
-        )
-        outputs = hf_model.generate(
-            pixel_values,
-            input_ids,
-            do_sample=True,
-            num_beams=5,
-            max_length=30,
-            min_length=1,
-            top_p=0.9,
-            repetition_penalty=1.0,
-            length_penalty=1.0,
-            temperature=1,
-        )
-        output_text = processor.batch_decode(outputs, skip_special_tokens=True)
-        output_text = [text.strip() for text in output_text]
-        print("Original generation:", original_outputs)
-        print("HF generation:", output_text)
-
-    if pytorch_dump_folder_path is not None:
-        processor.save_pretrained(pytorch_dump_folder_path)
-        hf_model.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        processor.push_to_hub(f"nielsr/{model_name}")
-        hf_model.push_to_hub(f"nielsr/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    choices = [
-        "blip2-opt-2.7b",
-        "blip2-opt-6.7b",
-        "blip2-opt-2.7b-coco",
-        "blip2-opt-6.7b-coco",
-        "blip2-flan-t5-xl",
-        "blip2-flan-t5-xl-coco",
-        "blip2-flan-t5-xxl",
-        "blip2-itm-vit-g",
-        "blip2-itm-vit-g-coco",
-    ]
-    parser.add_argument(
-        "--model_name",
-        default="blip2-opt-2.7b",
-        choices=choices,
-        type=str,
-        help="Path to hf config.json of model to convert",
-    )
-    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument(
-        "--push_to_hub",
-        action="store_true",
-        help="Whether to push the model and processor to the hub after converting",
-    )
-    # note: this script is tested on 2 GPUs, as models are compared in float32,
-    # which requires quite some memory. Hence loading both on a
-    # separate device is the easiest to compare
-    parser.add_argument(
-        "--lavis_device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
-    )
-    parser.add_argument(
-        "--hf_model_device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
-    )
-
-    args = parser.parse_args()
-
-    convert_blip2_checkpoint(
-        args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.lavis_device, args.hf_model_device
-    )

src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py

@@ -1,254 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert BigScience BLOOM checkpoint."""
-
-import argparse
-import json
-import os
-import re
-
-import torch
-
-from transformers import BloomConfig, BloomModel
-from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-WEIGHTS_TO_AVERAGE_ENDSWITH = [
-    "word_embeddings_layernorm.weight",
-    "word_embeddings_layernorm.bias",
-    "input_layernorm.weight",
-    "input_layernorm.bias",
-    "post_attention_layernorm.weight",
-    "post_attention_layernorm.bias",
-    "self_attention.dense.bias",
-    "mlp.dense_4h_to_h.bias",
-    "ln_f.weight",
-    "ln_f.bias",
-]
-
-WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN = [
-    "mlp.dense_4h_to_h.weight",
-    "self_attention.dense.weight",
-]
-
-
-def layer_name_mapping(key, file):
-    """Convert Megatron-DeepSpeed TP/PP weights mapping in transformers PP only"""
-    # Handle first and last layers
-    layer_rename_map = {
-        "word_embeddings.weight": "word_embeddings.weight",
-        "word_embeddings.norm.weight": "word_embeddings_layernorm.weight",
-        "word_embeddings.norm.bias": "word_embeddings_layernorm.bias",
-        "weight": "ln_f.weight",
-        "bias": "ln_f.bias",
-    }
-
-    if key in layer_rename_map:
-        return layer_rename_map[key]
-
-    # Handle transformer blocks
-    layer_number = int(re.match(r".*layer_(\d*).*", file)[1])
-    layer_number -= 3
-    return f"h.{layer_number}." + key
-
-
-def get_dtype_size(dtype):
-    if dtype == torch.bool:
-        return 1 / 8
-    bit_search = re.search(r"[^\d](\d+)$", str(dtype))
-    if bit_search is None:
-        raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
-    bit_size = int(bit_search.groups()[0])
-    return bit_size // 8
-
-
-def convert_bloom_checkpoint_to_pytorch(
-    bloom_checkpoint_path, bloom_config_file, pytorch_dump_folder_path, shard_model, pretraining_tp
-):
-    # Construct model
-    if bloom_config_file == "":
-        config = BloomConfig()
-    else:
-        config = BloomConfig.from_json_file(bloom_config_file)
-
-    if shard_model:
-        file_names = os.listdir(bloom_checkpoint_path)
-        file_names = sorted(filter(lambda s: s.startswith("layer") and "model_00" in s, file_names))
-
-        index_dict = {"weight_map": {}, "metadata": {}}
-        total_size = 0
-
-        missing_keys = None
-
-        config = BloomConfig()
-
-        for j, file in enumerate(file_names):
-            print("Processing file: {}".format(file))
-            tensors = None
-
-            for i in range(pretraining_tp):
-                # load all TP files
-                f_name = file.replace("model_00", f"model_0{i}")
-                temp = torch.load(os.path.join(bloom_checkpoint_path, f_name), map_location="cpu")
-
-                # Rename keys in the transformers names
-                keys = list(temp.keys())
-                for key in keys:
-                    temp[layer_name_mapping(key, file)] = temp.pop(key)
-
-                if tensors is None:
-                    tensors = temp
-                else:
-                    for key in tensors.keys():
-                        if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
-                            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
-                            tensors[key] += temp[key]
-                        else:
-                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
-                            cat_dim = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
-                            # We concatenate these weights accross TP ranks
-                            tensors[key] = torch.cat([tensors[key], temp[key]], dim=cat_dim)
-
-            # Divide by the number of TP the weights we want to average
-            for key in tensors.keys():
-                if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
-                    tensors[key] = tensors[key] / pretraining_tp
-            torch.save(
-                tensors,
-                os.path.join(
-                    pytorch_dump_folder_path,
-                    "pytorch_model_{}-of-{}.bin".format(str(j + 1).zfill(5), str(len(file_names)).zfill(5)),
-                ),
-            )
-
-            for key in tensors.keys():
-                value = tensors[key]
-                total_size += value.numel() * get_dtype_size(value.dtype)
-                if key not in index_dict["weight_map"]:
-                    index_dict["weight_map"][key] = "pytorch_model_{}-of-{}.bin".format(
-                        str(j + 1).zfill(5), str(len(file_names)).zfill(5)
-                    )
-
-        config = BloomConfig()
-        pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
-        index_dict["metadata"]["total_size"] = total_size
-        with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
-            f.write(config.to_json_string())
-        with open(os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME + ".index.json"), "w", encoding="utf-8") as f:
-            json_config = json.dumps(index_dict, indent=2, sort_keys=True) + "\n"
-            f.write(json_config)
-    else:
-        model = BloomModel(config)
-
-        file_names = os.listdir(bloom_checkpoint_path)
-        file_names = sorted(filter(lambda s: s.startswith("layer") and "model_00" in s, file_names))
-
-        missing_keys = None
-        for i, file in enumerate(file_names):
-            tensors = None
-            for i in range(pretraining_tp):
-                # load all TP files
-                f_name = file.replace("model_00", f"model_0{i}")
-                temp = torch.load(os.path.join(bloom_checkpoint_path, f_name), map_location="cpu")
-
-                # Rename keys in the transformers names
-                keys = list(temp.keys())
-                for key in keys:
-                    temp[layer_name_mapping(key, file)] = temp.pop(key)
-
-                if tensors is None:
-                    tensors = temp
-                else:
-                    for key in tensors.keys():
-                        # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
-                        if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
-                            tensors[key] += temp[key]
-                        else:
-                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
-                            cat_dim = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
-                            # We concatenate these weights accross TP ranks
-                            tensors[key] = torch.cat([tensors[key], temp[key]], dim=cat_dim)
-
-            # Divide by the number of TP the weights we want to average
-            for key in tensors.keys():
-                if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
-                    tensors[key] = tensors[key] / pretraining_tp
-
-            other_keys = model.load_state_dict(tensors, strict=False)
-            assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
-            if missing_keys is None:
-                missing_keys = set(other_keys.missing_keys)
-            else:
-                missing_keys = missing_keys.intersection(set(other_keys.missing_keys))
-
-        assert not missing_keys, f"The keys {missing_keys} are missing"
-
-        # Save pytorch-model
-        os.makedirs(pytorch_dump_folder_path, exist_ok=True)
-        pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
-        pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
-        print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}")
-        if config.torch_dtype is not None:
-            model = model.to(config.torch_dtype)
-        torch.save(model.state_dict(), pytorch_weights_dump_path)
-        print(f"Save configuration file to {pytorch_config_dump_path}")
-        with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
-            f.write(config.to_json_string())
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--bloom_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the Megatron-LM checkpoint path.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    parser.add_argument(
-        "--bloom_config_file",
-        default="",
-        type=str,
-        help=(
-            "An optional config json file corresponding to the pre-trained model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--shard_model",
-        action="store_true",
-        help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
-    )
-    parser.add_argument(
-        "--pretraining_tp",
-        default=4,
-        type=int,
-        help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
-    )
-    args = parser.parse_args()
-    convert_bloom_checkpoint_to_pytorch(
-        args.bloom_checkpoint_path,
-        args.bloom_config_file,
-        args.pytorch_dump_folder_path,
-        args.shard_model,
-        args.pretraining_tp,
-    )

src/transformers/models/bros/convert_bros_to_pytorch.py

@@ -1,145 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Bros checkpoints."""
-
-import argparse
-
-import bros  # original repo
-import torch
-
-from transformers import BrosConfig, BrosModel, BrosProcessor
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_configs(model_name):
-    bros_config = BrosConfig.from_pretrained(model_name)
-    return bros_config
-
-
-def remove_ignore_keys_(state_dict):
-    ignore_keys = [
-        "embeddings.bbox_sinusoid_emb.inv_freq",
-    ]
-    for k in ignore_keys:
-        state_dict.pop(k, None)
-
-
-def rename_key(name):
-    if name == "embeddings.bbox_projection.weight":
-        name = "bbox_embeddings.bbox_projection.weight"
-
-    if name == "embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq":
-        name = "bbox_embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq"
-
-    if name == "embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq":
-        name = "bbox_embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq"
-
-    return name
-
-
-def convert_state_dict(orig_state_dict, model):
-    # rename keys
-    for key in orig_state_dict.copy().keys():
-        val = orig_state_dict.pop(key)
-        orig_state_dict[rename_key(key)] = val
-
-    # remove ignore keys
-    remove_ignore_keys_(orig_state_dict)
-
-    return orig_state_dict
-
-
-def convert_bros_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
-    # load original model
-    original_model = bros.BrosModel.from_pretrained(model_name).eval()
-
-    # load HuggingFace Model
-    bros_config = get_configs(model_name)
-    model = BrosModel.from_pretrained(model_name, config=bros_config)
-    model.eval()
-
-    state_dict = original_model.state_dict()
-    new_state_dict = convert_state_dict(state_dict, model)
-    model.load_state_dict(new_state_dict)
-
-    # verify results
-
-    # original BROS model require 4 points (8 float values) for each bbox, prepare bbox with [batch_size, seq_len, 8] shape
-    bbox = torch.tensor(
-        [
-            [
-                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
-                [0.4396, 0.6720, 0.4659, 0.6720, 0.4659, 0.6850, 0.4396, 0.6850],
-                [0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850],
-                [0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850],
-                [0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000],
-                [0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000],
-                [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
-            ]
-        ]
-    )
-
-    processor = BrosProcessor.from_pretrained(model_name)
-
-    encoding = processor("His name is Rocco.", return_tensors="pt")
-    encoding["bbox"] = bbox
-
-    original_hidden_states = original_model(**encoding).last_hidden_state
-    # pixel_values = processor(image, return_tensors="pt").pixel_values
-
-    last_hidden_states = model(**encoding).last_hidden_state
-
-    assert torch.allclose(original_hidden_states, last_hidden_states, atol=1e-4)
-
-    if pytorch_dump_folder_path is not None:
-        print(f"Saving model and processor to {pytorch_dump_folder_path}")
-        model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        model.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model")
-        processor.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="jinho8345/bros-base-uncased",
-        required=False,
-        type=str,
-        help="Name of the original model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        required=False,
-        type=str,
-        help="Path to the output PyTorch model directory.",
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        action="store_true",
-        help="Whether or not to push the converted model and processor to the 🤗 hub.",
-    )
-
-    args = parser.parse_args()
-    convert_bros_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/byt5/convert_byt5_original_tf_checkpoint_to_pytorch.py

@@ -1,59 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The T5 authors and HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert T5 checkpoint."""
-
-import argparse
-
-from transformers import T5Config, T5ForConditionalGeneration, load_tf_weights_in_t5
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
-    # Initialise PyTorch model
-    config = T5Config.from_json_file(config_file)
-    print(f"Building PyTorch model from configuration: {config}")
-    model = T5ForConditionalGeneration(config)
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_t5(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    model.save_pretrained(pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

src/transformers/models/canine/convert_canine_original_tf_checkpoint_to_pytorch.py

@@ -1,65 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert CANINE checkpoint."""
-
-import argparse
-
-from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path):
-    # Initialize PyTorch model
-    config = CanineConfig()
-    model = CanineModel(config)
-    model.eval()
-
-    print(f"Building PyTorch model from configuration: {config}")
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_canine(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model (weights and configuration)
-    print(f"Save PyTorch model to {pytorch_dump_path}")
-    model.save_pretrained(pytorch_dump_path)
-
-    # Save tokenizer files
-    tokenizer = CanineTokenizer()
-    print(f"Save tokenizer files to {pytorch_dump_path}")
-    tokenizer.save_pretrained(pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
-    )
-    parser.add_argument(
-        "--pytorch_dump_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to a folder where the PyTorch model will be placed.",
-    )
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)

src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py

@@ -1,476 +0,0 @@
-# Copyright 2024 Meta Inc. and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import gc
-import json
-import os
-
-import requests
-import torch
-import yaml
-from accelerate import init_empty_weights
-from PIL import Image
-
-from transformers import (
-    ChameleonConfig,
-    ChameleonForConditionalGeneration,
-    ChameleonImageProcessor,
-    ChameleonProcessor,
-)
-
-
-try:
-    from transformers import LlamaTokenizerFast
-except ImportError:
-    raise ValueError(
-        "Chameleon conversion supports only FastTokenizer and LlamaTokenizerFast can't be imported! "
-        "Update your `tokenizers` library and re-run the tokenizer conversion."
-    )
-
-"""
-Sample usage:
-
-```
-python src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py \
-    --input_dir /path/to/downloaded/chameleon/weights --model_size 7B --output_dir /output/path
-```
-
-Thereafter, models can be loaded via:
-
-```py
-from transformers import ChameleonForConditionalGeneration, LlamaTokenizerFast
-
-model = ChameleonForConditionalGeneration.from_pretrained("/output/path")
-tokenizer = LlamaTokenizerFast.from_pretrained("/output/path")
-```
-
-Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
-come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
-"""
-
-NUM_SHARDS = {
-    "7B": 1,
-    "30B": 4,
-}
-
-VOCAB_SIZE = 65536
-
-
-def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
-    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
-
-
-def read_json(path):
-    with open(path, "r") as f:
-        return json.load(f)
-
-
-def write_json(text, path):
-    with open(path, "w") as f:
-        json.dump(text, f)
-
-
-def write_model(model_path, input_base_path, model_size, chameleon_version=1):
-    os.makedirs(model_path, exist_ok=True)
-    input_model_path = os.path.join(input_base_path, "models", model_size.lower())
-    params_path = os.path.join(input_model_path, "params.json")
-    consolidate_params_path = os.path.join(input_model_path, "consolidate_params.json")
-
-    params = read_json(params_path)
-    if os.path.isfile(consolidate_params_path):
-        params = {**params, **read_json(consolidate_params_path)}
-    num_shards = NUM_SHARDS[model_size]
-    model_parallel_size = params["model_parallel_size"]
-    params = params.get("model", params)
-    n_layers = params["n_layers"]
-    n_heads = params["n_heads"]
-    n_heads_per_shard = n_heads // num_shards
-    dim = params["dim"]
-    dims_per_head = dim // n_heads
-    base = params.get("rope_theta", 10000.0)
-    swin_norm = params["swin_norm"]
-    if base > 10000.0:
-        max_position_embeddings = 16384
-    else:
-        # Depending on the Chameleon version, the default max_position_embeddings has different values.
-        if chameleon_version == 1:
-            max_position_embeddings = 4096
-        else:
-            raise NotImplementedError(
-                f"Version {chameleon_version} of chameleon is not supported yet. "
-                "Current supported versions of chameleon are [1]."
-            )
-
-    if params.get("n_kv_heads", None) is not None:
-        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
-        num_local_key_value_heads = n_heads_per_shard // num_key_value_heads
-        key_value_dim = dim // num_key_value_heads
-    else:  # compatibility with other checkpoints
-        num_key_value_heads = n_heads
-        num_local_key_value_heads = n_heads_per_shard
-        key_value_dim = dim
-
-    print(f"Fetching all parameters from the checkpoint at {input_model_path}.")
-    # Load weights
-    if num_shards == 1:
-        # Not sharded
-        # (The sharded implementation would also work, but this is simpler.)
-        loaded = None
-        for possible_name in ["consolidated.pth", "consolidated.00.pth"]:
-            possible_path = os.path.join(input_model_path, possible_name)
-            if os.path.exists(possible_path):
-                loaded = torch.load(possible_path, map_location="cpu")
-                break
-        assert loaded is not None
-    else:
-        # Sharded
-        loaded = [
-            torch.load(os.path.join(input_model_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
-            for i in range(num_shards)
-        ]
-
-    # permute for sliced rotary
-    def permute(w, n_heads, dim1=dim, dim2=dim):
-        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
-
-    # Load weights to the state dict
-    state_dict = {}
-    for layer_i in range(n_layers):
-        if num_shards == 1:
-            # Unsharded
-            state_dict.update(
-                {
-                    f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
-                        loaded[f"layers.{layer_i}.attention.wq.weight"], n_heads=n_heads
-                    ),
-                    f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
-                        loaded[f"layers.{layer_i}.attention.wk.weight"],
-                        n_heads=num_key_value_heads,
-                        dim1=key_value_dim,
-                    ),
-                    f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
-                    f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
-                    f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
-                    f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
-                    f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
-                    f"model.layers.{layer_i}.input_layernorm.weight": loaded[
-                        f"layers.{layer_i}.attention_norm.weight"
-                    ],
-                    f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[
-                        f"layers.{layer_i}.ffn_norm.weight"
-                    ],
-                }
-            )
-            # qk_layernorm (see https://github.com/huggingface/transformers/pull/31534#issuecomment-2207354677)
-            state_dict[f"model.layers.{layer_i}.self_attn.q_norm.weight"] = (
-                loaded[f"layers.{layer_i}.attention.q_normalization.weight"]
-                .view(dims_per_head // 2, 2)
-                .t()
-                .reshape(1, -1)
-                .repeat_interleave(n_heads, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.q_norm.bias"] = (
-                loaded[f"layers.{layer_i}.attention.q_normalization.bias"]
-                .view(dims_per_head // 2, 2)
-                .t()
-                .reshape(1, -1)
-                .repeat_interleave(n_heads, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_norm.weight"] = (
-                loaded[f"layers.{layer_i}.attention.k_normalization.weight"]
-                .view(dims_per_head // 2, 2)
-                .t()
-                .reshape(1, -1)
-                .repeat_interleave(num_key_value_heads, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_norm.bias"] = (
-                loaded[f"layers.{layer_i}.attention.k_normalization.bias"]
-                .view(dims_per_head // 2, 2)
-                .t()
-                .reshape(1, -1)
-                .repeat_interleave(num_key_value_heads, 0)
-            )
-
-        else:
-            # Sharded
-            state_dict.update(
-                {
-                    f"model.layers.{layer_i}.input_layernorm.weight": torch.stack(
-                        [l[f"layers.{layer_i}.attention_norm.weight"] for l in loaded]
-                    ).mean(dim=0),
-                    f"model.layers.{layer_i}.post_attention_layernorm.weight": torch.stack(
-                        [l[f"layers.{layer_i}.ffn_norm.weight"] for l in loaded]
-                    ).mean(dim=0),
-                }
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
-                        for i in range(num_shards)
-                    ],
-                    dim=0,
-                ).reshape(dim, dim),
-                n_heads=n_heads,
-            )
-
-            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
-                            num_local_key_value_heads, dims_per_head, dim
-                        )
-                        for i in range(num_shards)
-                    ],
-                    dim=0,
-                ).reshape(key_value_dim, dim),
-                n_heads=num_key_value_heads,
-                dim1=key_value_dim,
-            )
-
-            # qk_layernorm (see https://github.com/huggingface/transformers/pull/31534#issuecomment-2207354677)
-            state_dict[f"model.layers.{layer_i}.self_attn.q_norm.weight"] = (
-                torch.cat([l[f"layers.{layer_i}.attention.q_normalization.weight"].unsqueeze(0) for l in loaded])
-                .view(num_shards, dims_per_head // 2, 2)
-                .transpose(1, 2)
-                .reshape(num_shards, -1)
-                .repeat_interleave(n_heads // num_shards, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.q_norm.bias"] = (
-                torch.cat([l[f"layers.{layer_i}.attention.q_normalization.bias"].unsqueeze(0) for l in loaded])
-                .view(num_shards, dims_per_head // 2, 2)
-                .transpose(1, 2)
-                .reshape(num_shards, -1)
-                .repeat_interleave(n_heads // num_shards, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_norm.weight"] = (
-                torch.cat([l[f"layers.{layer_i}.attention.k_normalization.weight"].unsqueeze(0) for l in loaded])
-                .view(num_shards, dims_per_head // 2, 2)
-                .transpose(1, 2)
-                .reshape(num_shards, -1)
-                .repeat_interleave(num_key_value_heads // num_shards, 0)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_norm.bias"] = (
-                torch.cat([l[f"layers.{layer_i}.attention.k_normalization.bias"].unsqueeze(0) for l in loaded])
-                .view(num_shards, dims_per_head // 2, 2)
-                .transpose(1, 2)
-                .reshape(num_shards, -1)
-                .repeat_interleave(num_key_value_heads // num_shards, 0)
-            )
-
-            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
-                [
-                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
-                        num_local_key_value_heads, dims_per_head, dim
-                    )
-                    for i in range(num_shards)
-                ],
-                dim=0,
-            ).reshape(key_value_dim, dim)
-
-            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
-            )
-
-    if num_shards == 1:
-        # Unsharded
-        state_dict.update(
-            {
-                "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
-                "model.norm.weight": loaded["norm.weight"],
-                "lm_head.weight": loaded["output.weight"],
-            }
-        )
-    else:
-        state_dict.update(
-            {
-                "model.embed_tokens.weight": torch.cat(
-                    [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1
-                ),
-                "model.norm.weight": torch.stack([loaded[i]["norm.weight"] for i in range(num_shards)]).mean(dim=0),
-                "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
-            }
-        )
-
-    # Load VQGAN weights
-    vqgan_path = os.path.join(input_base_path, "tokenizer/vqgan.ckpt")
-    vqgan_state_dict = torch.load(vqgan_path, map_location="cpu")["state_dict"]
-    for k, v in vqgan_state_dict.items():
-        if "decoder" in k:
-            continue  # we dont do image generation yet
-        state_dict[f"model.vqmodel.{k}"] = v
-
-    # Write configs
-    ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
-    multiple_of = params["multiple_of"] if "multiple_of" in params else 256
-
-    with open(os.path.join(input_base_path, "tokenizer/text_tokenizer.json")) as tokenizer_file:
-        tokenizer_config = json.load(tokenizer_file)
-        vocabulary_map = tokenizer_config["model"]["vocab"]
-        vocabulary_map["<image>"] = vocabulary_map[
-            "<reserved08707>"
-        ]  # use a reserved token instead of adding a new one
-        del vocabulary_map["<reserved08707>"]
-
-        for token in tokenizer_config["added_tokens"]:
-            if token["content"] == "<reserved08707>":
-                token["content"] = "<image>"
-
-    with open(os.path.join(input_base_path, "tokenizer/text_tokenizer_modified.json"), "w") as f:
-        json.dump(tokenizer_config, f)  # save the new file to init tokenizer later
-
-    vq_keys_to_replace = [
-        ("ch", "base_channels"),
-        ("out_ch", "out_channels"),
-        ("n_embed", "num_embeddings"),
-        ("ch_mult", "channel_multiplier"),
-        ("double_z", "double_latent"),
-        ("z_channels", "latent_channels"),
-    ]
-    with open(os.path.join(input_base_path, "tokenizer/vqgan.yaml")) as vqgan_cfg_file:
-        vq_config = yaml.safe_load(vqgan_cfg_file)["model"]["params"]
-        vq_config.update(**vq_config["ddconfig"])
-        for old, new in vq_keys_to_replace:
-            vq_config[new] = vq_config[old]
-        del vq_config["ddconfig"]
-        del vq_config["ckpt_path"]
-        del vq_config["lossconfig"]
-
-    config = ChameleonConfig(
-        hidden_size=dim,
-        intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
-        num_attention_heads=params["n_heads"],
-        num_hidden_layers=params["n_layers"],
-        rms_norm_eps=params["norm_eps"],
-        num_key_value_heads=num_key_value_heads,
-        vocab_size=VOCAB_SIZE,
-        rope_theta=base,
-        max_position_embeddings=max_position_embeddings,
-        model_parallel_size=model_parallel_size,
-        swin_norm=swin_norm,
-        vq_config=vq_config,
-        vocabulary_map=vocabulary_map,
-    )
-    with init_empty_weights():
-        model = ChameleonForConditionalGeneration(config)
-
-    model.load_state_dict(state_dict, assign=True, strict=False)
-    model.save_pretrained(model_path, safe_serialization=True)
-
-    # Load and save the processor
-    tokenizer = LlamaTokenizerFast(
-        tokenizer_file=os.path.join(input_base_path, "tokenizer/text_tokenizer_modified.json"), legacy=False
-    )
-    tokenizer.sep_token_id = 8710  # assign <reserved08706> to sep so that we can append it after input text
-    tokenizer.pad_token_id = 1  # assing <pad> to special pad_token
-    image_processor = ChameleonImageProcessor()
-    processor = ChameleonProcessor(image_processor=image_processor, tokenizer=tokenizer)
-    processor.save_pretrained(model_path)
-
-    # Make space so we can load the model properly now.
-    del state_dict
-    del loaded
-    del vqgan_state_dict
-    gc.collect()
-
-    # Short inference on a few examples to check if generation makes sense
-    # taken from https://github.com/facebookresearch/chameleon/blob/7a72f40aa5f462965c8374f25257f55b65b25ff4/data/prompts_for_human_evaluations.jsonl
-    print("Loading the checkpoint in a Chameleon model...")
-    print("*" * 100)
-    model = ChameleonForConditionalGeneration.from_pretrained(
-        model_path, attn_implementation="eager", torch_dtype=torch.bfloat16, device_map="auto"
-    )
-    processor = ChameleonProcessor.from_pretrained(model_path)
-
-    prompt = "I'm very intrigued by this work of art:<image>Please tell me about the artist."
-    image = Image.open(
-        requests.get(
-            "https://uploads4.wikiart.org/images/paul-klee/death-for-the-idea-1915.jpg!Large.jpg", stream=True
-        ).raw
-    )
-    inputs = processor(prompt, images=image, return_tensors="pt").to(model.device, torch.bfloat16)
-    length = inputs.input_ids.shape[1]
-
-    out = model.generate(**inputs, max_new_tokens=40, do_sample=False)
-    generated_text = processor.batch_decode(out[:, length:], skip_special_tokens=True)[0]
-
-    print(f"Generation for single-image: {generated_text}")
-    print("*" * 100)
-
-    # Multi-image example
-    prompt = "I used to know a lot about constellations when I was younger, but as I grew older, I forgot most of what I knew. These are the only two constellations that I really remember now.<image><image>I would like for you to tell me about 3 more constellations and give me a little bit of history about the constellation."
-    image = Image.open(
-        requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
-    )
-    image_2 = Image.open(
-        requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
-    )
-
-    inputs = processor(prompt, images=[image, image_2], return_tensors="pt").to(model.device, dtype=torch.bfloat16)
-    length = inputs.input_ids.shape[1]
-    out = model.generate(**inputs, max_new_tokens=50, do_sample=False)
-    generated_text = processor.batch_decode(out[:, length:], skip_special_tokens=True)[0]
-
-    print(f"Generation for multi-image: {generated_text}")
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input_dir",
-        help="Location of Chameleon weights",
-    )
-    parser.add_argument(
-        "--model_size",
-        choices=["7B", "30B"],
-        help=""
-        " models correspond to the finetuned versions, and are specific to the Chameleon official release. For more details on Chameleon, checkout the original repo: https://github.com/facebookresearch/chameleon",
-    )
-    parser.add_argument(
-        "--output_dir",
-        help="Location to write HF model",
-    )
-    parser.add_argument(
-        "--test_inference",
-        action="store_true",
-        help="Whether to load the model for generation to test it's converted correctly.",
-    )
-    # Different Chameleon versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
-    parser.add_argument(
-        "--chameleon_version",
-        choices=[1],
-        default=1,
-        type=int,
-        help="Version of the Chameleon model to convert",
-    )
-    args = parser.parse_args()
-    write_model(
-        model_path=args.output_dir,
-        input_base_path=args.input_dir,
-        model_size=args.model_size,
-        chameleon_version=args.chameleon_version,
-    )
-
-
-if __name__ == "__main__":
-    main()

src/transformers/models/chinese_clip/convert_chinese_clip_original_pytorch_to_hf.py

@@ -1,134 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OFA-Sys Team Authors and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-
-import torch
-
-from transformers import ChineseCLIPConfig, ChineseCLIPModel
-
-
-def copy_attn_layer(hf_attn_layer, pt_weights, prefix):
-    q_proj, k_proj, v_proj = pt_weights[f"{prefix}.in_proj_weight"].chunk(3, dim=0)
-    q_proj_bias, k_proj_bias, v_proj_bias = pt_weights[f"{prefix}.in_proj_bias"].chunk(3, dim=0)
-
-    out_proj_weights = pt_weights[f"{prefix}.out_proj.weight"]
-    out_proj_bias = pt_weights[f"{prefix}.out_proj.bias"]
-
-    hf_attn_layer.q_proj.weight.data = q_proj
-    hf_attn_layer.q_proj.bias.data = q_proj_bias
-
-    hf_attn_layer.k_proj.weight.data = k_proj
-    hf_attn_layer.k_proj.bias.data = k_proj_bias
-
-    hf_attn_layer.v_proj.weight.data = v_proj
-    hf_attn_layer.v_proj.bias.data = v_proj_bias
-
-    hf_attn_layer.out_proj.weight.data = out_proj_weights
-    hf_attn_layer.out_proj.bias.data = out_proj_bias
-
-
-def copy_mlp(hf_mlp, pt_weights, prefix):
-    copy_linear(hf_mlp.fc1, pt_weights, f"{prefix}.c_fc")
-    copy_linear(hf_mlp.fc2, pt_weights, f"{prefix}.c_proj")
-
-
-def copy_linear(hf_linear, pt_weights, prefix):
-    hf_linear.weight.data = pt_weights[f"{prefix}.weight"].data
-    hf_linear.bias.data = pt_weights[f"{prefix}.bias"].data
-
-
-def copy_layer(hf_layer, pt_weights, prefix):
-    # copy layer norms
-    copy_linear(hf_layer.layer_norm1, pt_weights, f"{prefix}.ln_1")
-    copy_linear(hf_layer.layer_norm2, pt_weights, f"{prefix}.ln_2")
-
-    # copy MLP
-    copy_mlp(hf_layer.mlp, pt_weights, f"{prefix}.mlp")
-
-    # copy attn
-    copy_attn_layer(hf_layer.self_attn, pt_weights, f"{prefix}.attn")
-
-
-def copy_layers(hf_layers, pt_weights, prefix):
-    for layer_id, hf_layer in enumerate(hf_layers):
-        copy_layer(hf_layer, pt_weights, f"{prefix}.{layer_id}")
-
-
-def copy_text_model_and_projection(hf_model, pt_weights):
-    # copy projection
-    hf_model.text_projection.weight.data = pt_weights["text_projection"].data.T
-
-    # copy text encoder
-    for name, param in hf_model.text_model.named_parameters():
-        param.data = pt_weights[f"bert.{name}"].data
-
-
-def copy_vision_model_and_projection(hf_model, pt_weights):
-    # copy projection
-    hf_model.visual_projection.weight.data = pt_weights["visual.proj"].data.T
-
-    # copy layer norms
-    copy_linear(hf_model.vision_model.pre_layrnorm, pt_weights, "visual.ln_pre")
-    copy_linear(hf_model.vision_model.post_layernorm, pt_weights, "visual.ln_post")
-
-    # copy embeddings
-    hf_model.vision_model.embeddings.patch_embedding.weight.data = pt_weights["visual.conv1.weight"].data
-    hf_model.vision_model.embeddings.class_embedding.data = pt_weights["visual.class_embedding"].data
-    hf_model.vision_model.embeddings.position_embedding.weight.data = pt_weights["visual.positional_embedding"].data
-
-    # copy encoder
-    copy_layers(hf_model.vision_model.encoder.layers, pt_weights, "visual.transformer.resblocks")
-
-
-@torch.no_grad()
-def convert_chinese_clip_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None):
-    """
-    Copy/paste/tweak model's weights to transformers design.
-    """
-
-    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
-    config = ChineseCLIPConfig.from_pretrained(config_path)
-
-    hf_model = ChineseCLIPModel(config).eval()
-
-    pt_weights = torch.load(checkpoint_path, map_location="cpu")["state_dict"]
-    pt_weights = {(name[7:] if name.startswith("module.") else name): value for name, value in pt_weights.items()}
-
-    copy_text_model_and_projection(hf_model, pt_weights)
-    copy_vision_model_and_projection(hf_model, pt_weights)
-    hf_model.logit_scale.data = pt_weights["logit_scale"].data
-
-    hf_model.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        help="Path to the output folder storing converted hf PyTorch model.",
-    )
-    parser.add_argument(
-        "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
-    )
-    parser.add_argument(
-        "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
-    )
-    args = parser.parse_args()
-
-    convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
-    print("The conversion is finished!")

src/transformers/models/clap/convert_clap_original_pytorch_to_hf.py

@@ -1,133 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import re
-
-from laion_clap import CLAP_Module
-
-from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
-
-
-KEYS_TO_MODIFY_MAPPING = {
-    "text_branch": "text_model",
-    "audio_branch": "audio_model.audio_encoder",
-    "attn": "attention.self",
-    "self.proj": "output.dense",
-    "attention.self_mask": "attn_mask",
-    "mlp.fc1": "intermediate.dense",
-    "mlp.fc2": "output.dense",
-    "norm1": "layernorm_before",
-    "norm2": "layernorm_after",
-    "bn0": "batch_norm",
-}
-
-processor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")
-
-
-def init_clap(checkpoint_path, model_type, enable_fusion=False):
-    model = CLAP_Module(
-        amodel=model_type,
-        enable_fusion=enable_fusion,
-    )
-    model.load_ckpt(checkpoint_path)
-    return model
-
-
-def get_config_from_original(clap_model):
-    audio_config = {
-        "patch_embeds_hidden_size": clap_model.model.audio_branch.embed_dim,
-        "depths": clap_model.model.audio_branch.depths,
-        "hidden_size": clap_model.model.audio_projection[0].in_features,
-    }
-
-    text_config = {"hidden_size": clap_model.model.text_branch.pooler.dense.in_features}
-
-    return ClapConfig(audio_config=audio_config, text_config=text_config)
-
-
-def rename_state_dict(state_dict):
-    model_state_dict = {}
-
-    sequential_layers_pattern = r".*sequential.(\d+).*"
-    text_projection_pattern = r".*_projection.(\d+).*"
-
-    for key, value in state_dict.items():
-        # check if any key needs to be modified
-        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
-            if key_to_modify in key:
-                key = key.replace(key_to_modify, new_key)
-
-        if re.match(sequential_layers_pattern, key):
-            # replace sequential layers with list
-            sequential_layer = re.match(sequential_layers_pattern, key).group(1)
-
-            key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.")
-        elif re.match(text_projection_pattern, key):
-            projecton_layer = int(re.match(text_projection_pattern, key).group(1))
-
-            # Because in CLAP they use `nn.Sequential`...
-            transformers_projection_layer = 1 if projecton_layer == 0 else 2
-
-            key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.")
-
-        if "audio" and "qkv" in key:
-            # split qkv into query key and value
-            mixed_qkv = value
-            qkv_dim = mixed_qkv.size(0) // 3
-
-            query_layer = mixed_qkv[:qkv_dim]
-            key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
-            value_layer = mixed_qkv[qkv_dim * 2 :]
-
-            model_state_dict[key.replace("qkv", "query")] = query_layer
-            model_state_dict[key.replace("qkv", "key")] = key_layer
-            model_state_dict[key.replace("qkv", "value")] = value_layer
-        else:
-            model_state_dict[key] = value
-
-    return model_state_dict
-
-
-def convert_clap_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path, model_type, enable_fusion=False):
-    clap_model = init_clap(checkpoint_path, model_type, enable_fusion=enable_fusion)
-
-    clap_model.eval()
-    state_dict = clap_model.model.state_dict()
-    state_dict = rename_state_dict(state_dict)
-
-    transformers_config = get_config_from_original(clap_model)
-    transformers_config.audio_config.enable_fusion = enable_fusion
-    model = ClapModel(transformers_config)
-
-    # ignore the spectrogram embedding layer
-    model.load_state_dict(state_dict, strict=False)
-
-    model.save_pretrained(pytorch_dump_folder_path)
-    transformers_config.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
-    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
-    parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
-    parser.add_argument("--model_type", default="HTSAT-tiny", type=str, help="Whether to enable fusion or not")
-    args = parser.parse_args()
-
-    convert_clap_checkpoint(
-        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.model_type, args.enable_fusion
-    )

src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py

@@ -1,156 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-
-import torch
-from clip import load
-
-from transformers import CLIPConfig, CLIPModel
-
-
-def copy_attn_layer(hf_attn_layer, pt_attn_layer):
-    q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(3, dim=0)
-    q_proj_bias, k_proj_bias, v_proj_bias = pt_attn_layer.in_proj_bias.chunk(3, dim=0)
-
-    out_proj_weights = pt_attn_layer.out_proj.weight
-    out_proj_bias = pt_attn_layer.out_proj.bias
-
-    hf_attn_layer.q_proj.weight.data = q_proj
-    hf_attn_layer.q_proj.bias.data = q_proj_bias
-
-    hf_attn_layer.k_proj.weight.data = k_proj
-    hf_attn_layer.k_proj.bias.data = k_proj_bias
-
-    hf_attn_layer.v_proj.weight.data = v_proj
-    hf_attn_layer.v_proj.bias.data = v_proj_bias
-
-    hf_attn_layer.out_proj.weight = out_proj_weights
-    hf_attn_layer.out_proj.bias = out_proj_bias
-
-
-def copy_mlp(hf_mlp, pt_mlp):
-    copy_linear(hf_mlp.fc1, pt_mlp.c_fc)
-    copy_linear(hf_mlp.fc2, pt_mlp.c_proj)
-
-
-def copy_linear(hf_linear, pt_linear):
-    hf_linear.weight = pt_linear.weight
-    hf_linear.bias = pt_linear.bias
-
-
-def copy_layer(hf_layer, pt_layer):
-    # copy layer norms
-    copy_linear(hf_layer.layer_norm1, pt_layer.ln_1)
-    copy_linear(hf_layer.layer_norm2, pt_layer.ln_2)
-
-    # copy MLP
-    copy_mlp(hf_layer.mlp, pt_layer.mlp)
-
-    # copy attn
-    copy_attn_layer(hf_layer.self_attn, pt_layer.attn)
-
-
-def copy_layers(hf_layers, pt_layers):
-    for hf_layer, pt_layer in zip(hf_layers, pt_layers):
-        copy_layer(hf_layer, pt_layer)
-
-
-def copy_encoder(hf_encoder, pt_model):
-    # copy  embeds
-    hf_encoder.embeddings.token_embedding.weight = pt_model.token_embedding.weight
-    hf_encoder.embeddings.position_embedding.weight.data = pt_model.positional_embedding
-
-    # copy layer norm
-    copy_linear(hf_encoder.final_layer_norm, pt_model.ln_final)
-
-    # copy hidden layers
-    copy_layers(hf_encoder.encoder.layers, pt_model.transformer.resblocks)
-
-
-def copy_text_model_and_projection(hf_model, pt_model):
-    # copy projection
-    hf_model.text_projection.weight.data = pt_model.text_projection.data.T.contiguous()
-
-    # copy text encoder
-    copy_encoder(hf_model.text_model, pt_model)
-
-
-def copy_vison_model_and_projection(hf_model, pt_model):
-    # copy projection
-    hf_model.visual_projection.weight.data = pt_model.visual.proj.data.T.contiguous()
-
-    # copy layer norms
-    copy_linear(hf_model.vision_model.pre_layrnorm, pt_model.visual.ln_pre)
-    copy_linear(hf_model.vision_model.post_layernorm, pt_model.visual.ln_post)
-
-    # copy embeds
-    hf_model.vision_model.embeddings.patch_embedding.weight.data = pt_model.visual.conv1.weight.data
-    hf_model.vision_model.embeddings.class_embedding = pt_model.visual.class_embedding
-    hf_model.vision_model.embeddings.position_embedding.weight.data = pt_model.visual.positional_embedding.data
-
-    # copy encoder
-    copy_layers(hf_model.vision_model.encoder.layers, pt_model.visual.transformer.resblocks)
-
-
-@torch.no_grad()
-def convert_clip_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None):
-    """
-    Copy/paste/tweak model's weights to transformers design.
-    """
-    if config_path is not None:
-        config = CLIPConfig.from_pretrained(config_path)
-    else:
-        config = CLIPConfig(projection_dim=512, text_config={}, vision_config={})
-
-    hf_model = CLIPModel(config).eval()
-
-    pt_model, _ = load(checkpoint_path, device="cpu", jit=False)
-    pt_model = pt_model.eval()
-
-    copy_text_model_and_projection(hf_model, pt_model)
-    copy_vison_model_and_projection(hf_model, pt_model)
-    hf_model.logit_scale = pt_model.logit_scale
-
-    # Use `eos_token` so the example is more meaningful
-    input_ids = torch.tensor(
-        [
-            [config.text_config.bos_token_id]
-            + list(range(3, 77))
-            + [config.text_config.eos_token_id]
-            + [config.text_config.pad_token_id]
-        ]
-    )
-    pixel_values = torch.randn(1, 3, 224, 224)
-
-    hf_outputs = hf_model(input_ids=input_ids, pixel_values=pixel_values, return_dict=True)
-    hf_logits_per_image = hf_outputs.logits_per_image
-    hf_logits_per_text = hf_outputs.logits_per_text
-    pt_logits_per_image, pt_logits_per_text = pt_model(pixel_values, input_ids)
-
-    assert torch.allclose(hf_logits_per_image, pt_logits_per_image, atol=1e-3)
-    assert torch.allclose(hf_logits_per_text, pt_logits_per_text, atol=1e-3)
-
-    hf_model.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to OpenAI checkpoint")
-    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
-    args = parser.parse_args()
-
-    convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

src/transformers/models/clipseg/convert_clipseg_original_pytorch_to_hf.py

@@ -1,264 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Convert CLIPSeg checkpoints from the original repository. URL: https://github.com/timojl/clipseg."""
-
-import argparse
-
-import requests
-import torch
-from PIL import Image
-
-from transformers import (
-    CLIPSegConfig,
-    CLIPSegForImageSegmentation,
-    CLIPSegProcessor,
-    CLIPSegTextConfig,
-    CLIPSegVisionConfig,
-    CLIPTokenizer,
-    ViTImageProcessor,
-)
-
-
-def get_clipseg_config(model_name):
-    text_config = CLIPSegTextConfig()
-    vision_config = CLIPSegVisionConfig(patch_size=16)
-
-    use_complex_transposed_convolution = True if "refined" in model_name else False
-    reduce_dim = 16 if "rd16" in model_name else 64
-
-    config = CLIPSegConfig.from_text_vision_configs(
-        text_config,
-        vision_config,
-        use_complex_transposed_convolution=use_complex_transposed_convolution,
-        reduce_dim=reduce_dim,
-    )
-    return config
-
-
-def rename_key(name):
-    # update prefixes
-    if "clip_model" in name:
-        name = name.replace("clip_model", "clip")
-    if "transformer" in name:
-        if "visual" in name:
-            name = name.replace("visual.transformer", "vision_model")
-        else:
-            name = name.replace("transformer", "text_model")
-    if "resblocks" in name:
-        name = name.replace("resblocks", "encoder.layers")
-    if "ln_1" in name:
-        name = name.replace("ln_1", "layer_norm1")
-    if "ln_2" in name:
-        name = name.replace("ln_2", "layer_norm2")
-    if "c_fc" in name:
-        name = name.replace("c_fc", "fc1")
-    if "c_proj" in name:
-        name = name.replace("c_proj", "fc2")
-    if "attn" in name and "self" not in name:
-        name = name.replace("attn", "self_attn")
-    # text encoder
-    if "token_embedding" in name:
-        name = name.replace("token_embedding", "text_model.embeddings.token_embedding")
-    if "positional_embedding" in name and "visual" not in name:
-        name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
-    if "ln_final" in name:
-        name = name.replace("ln_final", "text_model.final_layer_norm")
-    # vision encoder
-    if "visual.class_embedding" in name:
-        name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
-    if "visual.conv1" in name:
-        name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
-    if "visual.positional_embedding" in name:
-        name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
-    if "visual.ln_pre" in name:
-        name = name.replace("visual.ln_pre", "vision_model.pre_layrnorm")
-    if "visual.ln_post" in name:
-        name = name.replace("visual.ln_post", "vision_model.post_layernorm")
-    # projection layers
-    if "visual.proj" in name:
-        name = name.replace("visual.proj", "visual_projection.weight")
-    if "text_projection" in name:
-        name = name.replace("text_projection", "text_projection.weight")
-    # decoder
-    if "trans_conv" in name:
-        name = name.replace("trans_conv", "transposed_convolution")
-    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
-        name = "decoder." + name
-    if "blocks" in name:
-        name = name.replace("blocks", "decoder.layers")
-    if "linear1" in name:
-        name = name.replace("linear1", "mlp.fc1")
-    if "linear2" in name:
-        name = name.replace("linear2", "mlp.fc2")
-    if "norm1" in name and "layer_" not in name:
-        name = name.replace("norm1", "layer_norm1")
-    if "norm2" in name and "layer_" not in name:
-        name = name.replace("norm2", "layer_norm2")
-
-    return name
-
-
-def convert_state_dict(orig_state_dict, config):
-    for key in orig_state_dict.copy().keys():
-        val = orig_state_dict.pop(key)
-
-        if key.startswith("clip_model") and "attn.in_proj" in key:
-            key_split = key.split(".")
-            if "visual" in key:
-                layer_num = int(key_split[4])
-                dim = config.vision_config.hidden_size
-                prefix = "vision_model"
-            else:
-                layer_num = int(key_split[3])
-                dim = config.text_config.hidden_size
-                prefix = "text_model"
-
-            if "weight" in key:
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
-                    dim : dim * 2, :
-                ]
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
-            else:
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
-                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
-        elif "self_attn" in key and "out_proj" not in key:
-            key_split = key.split(".")
-            layer_num = int(key_split[1])
-            dim = config.reduce_dim
-            if "weight" in key:
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
-            else:
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
-                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
-        else:
-            new_name = rename_key(key)
-            if "visual_projection" in new_name or "text_projection" in new_name:
-                val = val.T
-            orig_state_dict[new_name] = val
-
-    return orig_state_dict
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    image = Image.open(requests.get(url, stream=True).raw)
-    return image
-
-
-def convert_clipseg_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub):
-    config = get_clipseg_config(model_name)
-    model = CLIPSegForImageSegmentation(config)
-    model.eval()
-
-    state_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    # remove some keys
-    for key in state_dict.copy().keys():
-        if key.startswith("model"):
-            state_dict.pop(key, None)
-
-    # rename some keys
-    state_dict = convert_state_dict(state_dict, config)
-    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
-
-    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
-        raise ValueError("Missing keys that are not expected: {}".format(missing_keys))
-    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
-        raise ValueError(f"Unexpected keys: {unexpected_keys}")
-
-    image_processor = ViTImageProcessor(size=352)
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-    processor = CLIPSegProcessor(image_processor=image_processor, tokenizer=tokenizer)
-
-    image = prepare_img()
-    text = ["a glass", "something to fill", "wood", "a jar"]
-
-    inputs = processor(text=text, images=[image] * len(text), padding="max_length", return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    # verify values
-    expected_conditional = torch.tensor([0.1110, -0.1882, 0.1645])
-    expected_pooled_output = torch.tensor([0.2692, -0.7197, -0.1328])
-    if model_name == "clipseg-rd64-refined":
-        expected_masks_slice = torch.tensor(
-            [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]
-        )
-    elif model_name == "clipseg-rd64":
-        expected_masks_slice = torch.tensor(
-            [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]
-        )
-    elif model_name == "clipseg-rd16":
-        expected_masks_slice = torch.tensor(
-            [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]
-        )
-    else:
-        raise ValueError(f"Model name {model_name} not supported.")
-
-    assert torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3)
-    assert torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3)
-    assert torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3)
-    print("Looks ok!")
-
-    if pytorch_dump_folder_path is not None:
-        print(f"Saving model and processor to {pytorch_dump_folder_path}")
-        model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print(f"Pushing model and processor for {model_name} to the hub")
-        model.push_to_hub(f"CIDAS/{model_name}")
-        processor.push_to_hub(f"CIDAS/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--model_name",
-        default="clipseg-rd64",
-        type=str,
-        choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
-        help=(
-            "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
-            " reduce dimension)"
-        ),
-    )
-    parser.add_argument(
-        "--checkpoint_path",
-        default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
-        type=str,
-        help=(
-            "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
-            " the decoder weights."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-
-    args = parser.parse_args()
-    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/clvp/convert_clvp_to_hf.py

@@ -1,234 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Weights conversion script for CLVP
-"""
-
-import argparse
-import os
-
-import torch
-from huggingface_hub import hf_hub_download
-
-from transformers import ClvpConfig, ClvpModelForConditionalGeneration
-
-
-_MODELS = {
-    "clvp": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/clvp2.pth",
-    "decoder": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/autoregressive.pth",
-}
-
-dim = 1024
-sub_dim = dim // 16
-
-CLVP_ENCODERS_MAPPING = {
-    "text_transformer.transformer.attn_layers": "text_encoder_model",
-    "speech_transformer.transformer.attn_layers": "speech_encoder_model",
-    "text_transformer.transformer.norm": "text_encoder_model.final_layer_norm",
-    "speech_transformer.transformer.norm": "speech_encoder_model.final_layer_norm",
-    "to_text_latent": "text_encoder_model.projection",
-    "to_speech_latent": "speech_encoder_model.projection",
-    "text_emb": "text_encoder_model.token_embedding",
-    "speech_emb": "speech_encoder_model.token_embedding",
-    "1.wrap.net.0": "mlp.fc1",
-    "1.wrap.net.3": "mlp.fc2",
-    "1.wrap": "self_attn",
-    "to_out": "out_proj",
-    "to_q": "q_proj",
-    "to_k": "k_proj",
-    "to_v": "v_proj",
-    "temperature": "logit_scale",
-}
-
-CLVP_DECODER_MAPPING = {
-    "conditioning_encoder.init": "conditioning_encoder.mel_conv",
-    "conditioning_encoder.attn": "conditioning_encoder.mel_attn_blocks",
-    "mel_attn_blocks": "group_norms",
-    ".norm.weight": ".weight",
-    ".norm.bias": ".bias",
-    "text_embedding": "conditioning_encoder.text_token_embedding",
-    "text_pos_embedding.emb": "conditioning_encoder.text_position_embedding",
-    "final_norm": "speech_decoder_model.final_norm",
-    "mel_head": "speech_decoder_model.lm_head",
-    "gpt.ln_f": "speech_decoder_model.model.decoder.layer_norm",
-    "mel_embedding": "speech_decoder_model.model.decoder.input_embeds_layer",
-    "mel_pos_embedding.emb": "speech_decoder_model.model.decoder.position_embeds_layer",
-    "gpt.h": "speech_decoder_model.model.decoder.layers",
-    "ln_1": "input_layernorm",
-    "ln_2": "post_attention_layernorm",
-}
-
-
-def update_index(present_index):
-    if present_index % 2 == 0:
-        return int(present_index / 2)
-    else:
-        return int((present_index - 1) / 2)
-
-
-def convert_encoder_weights(original_weights):
-    converted_weights = {}
-    original_weights_keys = sorted(original_weights.keys())
-    for original_key in original_weights_keys:
-        updated_key = original_key
-        # for input_rmsnorm.weight and post_attention_rmsnorm.weight
-        if "0.0.g" in updated_key:
-            present_index = updated_key.split(".")[4]
-            if int(present_index) % 2 == 0:
-                updated_key = updated_key.replace("0.0.g", "input_rmsnorm.weight")
-            else:
-                updated_key = updated_key.replace("0.0.g", "post_attention_rmsnorm.weight")
-
-        if "transformer.attn_layers.layers" in updated_key:
-            present_index = updated_key.split(".")[4]
-            updated_index = update_index(int(present_index))
-            updated_key = updated_key.replace(
-                f"transformer.attn_layers.layers.{present_index}", f"transformer.attn_layers.layers.{updated_index}"
-            )
-
-        for k, v in CLVP_ENCODERS_MAPPING.items():
-            if k in updated_key:
-                updated_key = updated_key.replace(k, v)
-
-        converted_weights[updated_key] = original_weights.pop(original_key)
-
-    return converted_weights
-
-
-def convert_decoder_weights(original_weights):
-    converted_weights = {}
-    original_weights_keys = sorted(original_weights.keys())
-    for original_key in original_weights_keys:
-        updated_key = original_key
-        if len(updated_key.split(".")) > 3:
-            index, attr = updated_key.split(".")[2], updated_key.split(".")[-1]
-
-        # for decoder attention
-        if "attn.c_attn" in updated_key:
-            if attr == "weight":
-                slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).T.split(split_size=dim, dim=0)
-            else:
-                slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0)
-            converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.q_proj.{attr}"] = slice1
-            converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.k_proj.{attr}"] = slice2
-            converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.v_proj.{attr}"] = slice3
-            continue
-
-        if "attn.c_proj" in updated_key:
-            converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.out_proj.{attr}"] = (
-                original_weights[updated_key].squeeze(-1).T
-            )
-            continue
-
-        if "attn.bias" in updated_key or "attn.masked_bias" in updated_key or "text_head" in updated_key:
-            original_weights.pop(updated_key)
-            continue
-
-        # conditional encoder attention
-        if "qkv" in updated_key:
-            if attr == "weight":
-                slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).split(split_size=dim, dim=0)
-            else:
-                slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0)
-
-            indices = torch.arange(dim)
-            index1, index2, index3 = (
-                indices.unfold(0, sub_dim, sub_dim * 3).flatten(),
-                indices[sub_dim:].unfold(0, sub_dim, sub_dim * 3).flatten(),
-                indices[2 * sub_dim :].unfold(0, sub_dim, sub_dim * 3).flatten(),
-            )
-
-            converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.q_proj.{attr}"] = torch.concatenate(
-                [slice1[index1], slice2[index3], slice3[index2]],
-                axis=0,
-            )
-            converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.k_proj.{attr}"] = torch.concatenate(
-                [slice1[index2], slice2[index1], slice3[index3]],
-                axis=0,
-            )
-            converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.v_proj.{attr}"] = torch.concatenate(
-                [slice1[index3], slice2[index2], slice3[index1]],
-                axis=0,
-            )
-            continue
-
-        if "proj_out" in updated_key:
-            converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.out_proj.{attr}"] = original_weights[
-                updated_key
-            ].squeeze(-1)
-            continue
-
-        for k, v in CLVP_DECODER_MAPPING.items():
-            if k in updated_key:
-                updated_key = updated_key.replace(k, v)
-
-        converted_weights[updated_key] = original_weights.pop(original_key)
-
-    return converted_weights
-
-
-def _download(url: str, root: str):
-    repo_id = f"{url.split('/')[3]}/{url.split('/')[4]}"
-    filename = f"{url.split('/')[-2]}/{url.split('/')[-1]}"
-    hf_hub_download(
-        repo_id=repo_id,
-        filename=filename,
-        force_filename=root,
-        local_dir_use_symlinks=False,
-    )
-
-
-def convert_clvp_weights(checkpoint_path, pytorch_dump_folder_path):
-    converted_checkpoint = {}
-
-    for each_model_name, each_model_url in _MODELS.items():
-        each_model_path = os.path.join(checkpoint_path, each_model_url.split("/")[-1])
-        if not os.path.exists(each_model_path):
-            print(f"\n{each_model_name} was not found! Downloading it to {each_model_path}")
-            _download(url=each_model_url, root=each_model_path)
-
-        if each_model_name == "clvp":
-            clvp_checkpoint = torch.load(each_model_path, map_location="cpu")
-        else:
-            decoder_checkpoint = torch.load(each_model_path, map_location="cpu")
-
-    # Converting the weights
-    converted_checkpoint.update(**convert_encoder_weights(clvp_checkpoint))
-    converted_checkpoint.update(**convert_decoder_weights(decoder_checkpoint))
-
-    config = ClvpConfig.from_pretrained("susnato/clvp_dev")
-    model = ClvpModelForConditionalGeneration(config)
-
-    model.load_state_dict(converted_checkpoint, strict=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Model saved at {pytorch_dump_folder_path}!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # # Required parameters
-    parser.add_argument(
-        "--checkpoint_path", type=str, help="Path to the folder of downloaded checkpoints. (Please enter full path)"
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        help="Path to the output PyTorch model. (Please enter full path)",
-    )
-    args = parser.parse_args()
-
-    convert_clvp_weights(args.checkpoint_path, args.pytorch_dump_folder_path)

src/transformers/models/colpali/convert_colpali_weights_to_hf.py

@@ -1,214 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Convert ColPali weights from the original repository to the HF model format.
-
-Original repository: https://github.com/illuin-tech/colpali.
-
-NOTE: This script was originally run using `torch==2.5.1` and with:
-
-```bash
-python src/transformers/models/colpali/convert_colpali_weights_to_hf.py \
-    --model_id vidore/colpali-v1.2-merged \
-    --revision 89fd9736194236a1ecb7a9ec9b04f537f6f896af \
-    --original_vlm_name_or_path google/paligemma-3b-mix-448 \
-    --output_dir vidore/colpali-v1.2-hf-internal \
-    --push_to_hub
-
-python src/transformers/models/colpali/convert_colpali_weights_to_hf.py \
-    --model_id vidore/colpali-v1.3-merged \
-    --revision 5b955e3415a7c5468ab33119d98d6d45c3a5b2c3 \
-    --original_vlm_name_or_path google/paligemma-3b-mix-448 \
-    --output_dir vidore/colpali-v1.3-hf \
-    --push_to_hub
-```
-"""
-
-import argparse
-import glob
-from pathlib import Path
-from typing import Any, Dict, Optional
-
-import torch
-from huggingface_hub import snapshot_download
-from safetensors import safe_open
-
-from transformers import AutoConfig
-from transformers.models.colpali import ColPaliForRetrieval
-from transformers.models.colpali.configuration_colpali import ColPaliConfig
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-ORIGINAL_DTYPE = torch.bfloat16
-
-
-def rename_state_dict_keys(state_dict: Dict[str, Any]) -> Dict[str, Any]:
-    new_state_dict = {}
-    for key, value in state_dict.items():
-        new_key = key
-        if key.startswith("custom_text_proj"):
-            new_key = key.replace("custom_text_proj", "embedding_proj_layer")
-        if key.startswith("model."):
-            new_key = key.replace("model.", "vlm.", 1)
-        new_state_dict[new_key] = value
-    return new_state_dict
-
-
-def load_original_state_dict(model_id: str, revision: Optional[str] = None) -> Dict[str, torch.Tensor]:
-    directory_path = snapshot_download(
-        repo_id=model_id,
-        revision=revision,
-        allow_patterns=["*.safetensors"],
-    )
-
-    original_state_dict = {}
-    for path in glob.glob(f"{directory_path}/*"):
-        if path.endswith(".safetensors"):
-            with safe_open(path, framework="pt", device="cpu") as f:
-                for key in f.keys():
-                    original_state_dict[key] = f.get_tensor(key)
-
-    # Some weights are tied, so `lm.head`` is not saved. Let's clone to load state dict.
-    if "lm_head.weight" not in original_state_dict:
-        original_state_dict["vlm.language_model.lm_head.weight"] = original_state_dict[
-            "model.language_model.model.embed_tokens.weight"
-        ].clone()
-
-    return original_state_dict
-
-
-@torch.no_grad()
-def convert_colpali_weights_to_hf(
-    model_id: str,
-    output_dir: str,
-    push_to_hub: bool,
-    revision: Optional[str] = None,
-    original_vlm_name_or_path: Optional[str] = None,
-):
-    # Load the original model data
-    original_config = AutoConfig.from_pretrained(
-        model_id,
-        revision=revision,
-    )
-    if original_vlm_name_or_path is not None:
-        original_config._name_or_path = original_vlm_name_or_path
-    if hasattr(original_config, "architectures"):
-        delattr(original_config, "architectures")
-
-    original_state_dict = load_original_state_dict(model_id, revision=revision)
-
-    # Format the state_dict keys
-    original_state_dict = rename_state_dict_keys(original_state_dict)
-
-    # Create the new config
-    config = ColPaliConfig(
-        vlm_config=original_config,
-        embedding_dim=128,  # hardcoded in the original model
-    )
-    config.model_type = "colpali"
-    config.is_composition = False
-
-    # Load the untrained model
-    model = ColPaliForRetrieval(config=config).to("cpu").eval()
-    print("Created model with new config and randomly initialized weights")
-
-    # NOTE: The model was initialized with float32 weights. We need to convert it to the desired precision.
-    # There are two ways to set the model's dtype:
-    # - Using `model.from_pretrained(..., torch_dtype=dtype_precision)` doesn't convert the hyperparameters to the desired precision.
-    # - Using `model.to(dtype_precision)` converts all values - including the hyperparameters - to the desired precision.
-    # The following snippet allows a fine-grained control over the model's dtype, making sure that all
-    # the new weights' dtypes match the original model.
-    for param in model.parameters():
-        param.data = param.data.to(ORIGINAL_DTYPE)
-    print(f"Converted the new model weights to `{ORIGINAL_DTYPE}`")
-
-    # Load the original weights
-    model.load_state_dict(original_state_dict)
-    print("Loaded original model weights")
-
-    # Tie the weights (following ColPali's `__init__`` step)
-    if model.vlm.language_model._tied_weights_keys is not None:
-        model._tied_weights_keys = [f"vlm.language_model.{k}" for k in model.vlm.language_model._tied_weights_keys]
-
-    # Sanity check: ensure all keys are the same
-    state_dict_keys_old = set(original_state_dict.keys())
-    state_dict_keys_new = set(model.state_dict().keys())
-    disjoint_keys = state_dict_keys_old.symmetric_difference(state_dict_keys_new)
-    if disjoint_keys:
-        raise ValueError(f"Incompatible keys: {disjoint_keys}")
-
-    # Save the model
-    if push_to_hub:
-        model.push_to_hub(output_dir, private=True)
-        print(f"Model pushed to the hub at `{output_dir}`")
-    else:
-        Path(output_dir).mkdir(exist_ok=True, parents=True)
-        model.save_pretrained(output_dir)
-        print(f"Model saved to `{output_dir}`")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="""
-        This script converts the original ColPali model to the HF model format.
-
-        Example usage:
-        ```bash
-        python src/transformers/models/colpali/convert_colpali_weights_to_hf.py \
-            --model_id vidore/colpali-v1.2-merged \
-            --revision 89fd9736194236a1ecb7a9ec9b04f537f6f896af \
-            --original_vlm_name_or_path google/paligemma-3b-mix-448 \
-            --output_dir vidore/colpali-v1.2-hf \
-            --push_to_hub
-        ```
-        """
-    )
-    parser.add_argument(
-        "--model_id",
-        help="Model ID of the original model to convert",
-    )
-    parser.add_argument(
-        "--output_dir",
-        help="Location to write HF model and tokenizer",
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        help="Whether or not to push the model to the hub at `output_dir` instead of saving it locally",
-        action="store_true",
-        default=False,
-    )
-    parser.add_argument(
-        "--revision",
-        help="Revision of the model to download",
-        default=None,
-    )
-    parser.add_argument(
-        "--original_vlm_name_or_path",
-        help="Name or path of the original VLM backbone model",
-        default=None,
-    )
-    args = parser.parse_args()
-
-    convert_colpali_weights_to_hf(
-        model_id=args.model_id,
-        output_dir=args.output_dir,
-        push_to_hub=args.push_to_hub,
-        revision=args.revision,
-        original_vlm_name_or_path=args.original_vlm_name_or_path,
-    )

src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py

@@ -1,324 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Conditional DETR checkpoints."""
-
-import argparse
-import json
-from collections import OrderedDict
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import (
-    ConditionalDetrConfig,
-    ConditionalDetrForObjectDetection,
-    ConditionalDetrForSegmentation,
-    ConditionalDetrImageProcessor,
-)
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-rename_keys = []
-for i in range(6):
-    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
-    rename_keys.append(
-        (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
-    )
-    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
-    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
-    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
-    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
-    rename_keys.append(
-        (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
-    )
-    rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
-    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
-    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
-    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
-    )
-    rename_keys.append(
-        (
-            f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
-            f"decoder.layers.{i}.encoder_attn.out_proj.weight",
-        )
-    )
-    rename_keys.append(
-        (
-            f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
-            f"decoder.layers.{i}.encoder_attn.out_proj.bias",
-        )
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
-
-    # q, k, v projections in self/cross-attention in decoder for conditional DETR
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
-    )
-    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
-    )
-
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
-    )
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
-    )
-    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
-    )
-    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
-    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
-    rename_keys.append(
-        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
-    )
-
-# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
-# for conditional DETR, also convert reference point head and query scale MLP
-rename_keys.extend(
-    [
-        ("input_proj.weight", "input_projection.weight"),
-        ("input_proj.bias", "input_projection.bias"),
-        ("query_embed.weight", "query_position_embeddings.weight"),
-        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
-        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
-        ("class_embed.weight", "class_labels_classifier.weight"),
-        ("class_embed.bias", "class_labels_classifier.bias"),
-        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
-        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
-        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
-        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
-        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
-        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
-        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
-        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
-        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
-        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
-        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
-        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
-        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
-        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
-        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
-        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
-    ]
-)
-
-
-def rename_key(state_dict, old, new):
-    val = state_dict.pop(old)
-    state_dict[new] = val
-
-
-def rename_backbone_keys(state_dict):
-    new_state_dict = OrderedDict()
-    for key, value in state_dict.items():
-        if "backbone.0.body" in key:
-            new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model")
-            new_state_dict[new_key] = value
-        else:
-            new_state_dict[key] = value
-
-    return new_state_dict
-
-
-def read_in_q_k_v(state_dict, is_panoptic=False):
-    prefix = ""
-    if is_panoptic:
-        prefix = "conditional_detr."
-
-    # first: transformer encoder
-    for i in range(6):
-        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
-        in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
-        in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
-        # next, add query, keys and values (in that order) to the state dict
-        state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
-        state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
-        state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
-        state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
-        state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
-        state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-
-    return im
-
-
-@torch.no_grad()
-def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path):
-    """
-    Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure.
-    """
-
-    # load default config
-    config = ConditionalDetrConfig()
-    # set backbone and dilation attributes
-    if "resnet101" in model_name:
-        config.backbone = "resnet101"
-    if "dc5" in model_name:
-        config.dilation = True
-    is_panoptic = "panoptic" in model_name
-    if is_panoptic:
-        config.num_labels = 250
-    else:
-        config.num_labels = 91
-        repo_id = "huggingface/label-files"
-        filename = "coco-detection-id2label.json"
-        id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-        id2label = {int(k): v for k, v in id2label.items()}
-        config.id2label = id2label
-        config.label2id = {v: k for k, v in id2label.items()}
-
-    # load image processor
-    format = "coco_panoptic" if is_panoptic else "coco_detection"
-    image_processor = ConditionalDetrImageProcessor(format=format)
-
-    # prepare image
-    img = prepare_img()
-    encoding = image_processor(images=img, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-
-    logger.info(f"Converting model {model_name}...")
-
-    # load original model from torch hub
-    conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval()
-    state_dict = conditional_detr.state_dict()
-    # rename keys
-    for src, dest in rename_keys:
-        if is_panoptic:
-            src = "conditional_detr." + src
-        rename_key(state_dict, src, dest)
-    state_dict = rename_backbone_keys(state_dict)
-    # query, key and value matrices need special treatment
-    read_in_q_k_v(state_dict, is_panoptic=is_panoptic)
-    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
-    prefix = "conditional_detr.model." if is_panoptic else "model."
-    for key in state_dict.copy().keys():
-        if is_panoptic:
-            if (
-                key.startswith("conditional_detr")
-                and not key.startswith("class_labels_classifier")
-                and not key.startswith("bbox_predictor")
-            ):
-                val = state_dict.pop(key)
-                state_dict["conditional_detr.model" + key[4:]] = val
-            elif "class_labels_classifier" in key or "bbox_predictor" in key:
-                val = state_dict.pop(key)
-                state_dict["conditional_detr." + key] = val
-            elif key.startswith("bbox_attention") or key.startswith("mask_head"):
-                continue
-            else:
-                val = state_dict.pop(key)
-                state_dict[prefix + key] = val
-        else:
-            if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
-                val = state_dict.pop(key)
-                state_dict[prefix + key] = val
-    # finally, create HuggingFace model and load state dict
-    model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-    model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model")
-    # verify our conversion
-    original_outputs = conditional_detr(pixel_values)
-    outputs = model(pixel_values)
-    assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4)
-    assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4)
-    if is_panoptic:
-        assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4)
-
-    # Save model and image processor
-    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_name",
-        default="conditional_detr_resnet50",
-        type=str,
-        help="Name of the CONDITIONAL_DETR model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

src/transformers/models/convbert/convert_convbert_original_tf1_checkpoint_to_pytorch_and_tf2.py

@@ -1,57 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert ConvBERT checkpoint."""
-
-import argparse
-
-from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_orig_tf1_checkpoint_to_pytorch(tf_checkpoint_path, convbert_config_file, pytorch_dump_path):
-    conf = ConvBertConfig.from_json_file(convbert_config_file)
-    model = ConvBertModel(conf)
-
-    model = load_tf_weights_in_convbert(model, conf, tf_checkpoint_path)
-    model.save_pretrained(pytorch_dump_path)
-
-    tf_model = TFConvBertModel.from_pretrained(pytorch_dump_path, from_pt=True)
-    tf_model.save_pretrained(pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--convbert_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help=(
-            "The config json file corresponding to the pre-trained ConvBERT model. \n"
-            "This specifies the model architecture."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_orig_tf1_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)

src/transformers/models/convnext/convert_convnext_to_pytorch.py

@@ -1,242 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert ConvNext checkpoints from the original repository.
-
-URL: https://github.com/facebookresearch/ConvNeXt"""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_convnext_config(checkpoint_url):
-    config = ConvNextConfig()
-
-    if "tiny" in checkpoint_url:
-        depths = [3, 3, 9, 3]
-        hidden_sizes = [96, 192, 384, 768]
-    if "small" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [96, 192, 384, 768]
-    if "base" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [128, 256, 512, 1024]
-    if "large" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [192, 384, 768, 1536]
-    if "xlarge" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [256, 512, 1024, 2048]
-
-    if "1k" in checkpoint_url:
-        num_labels = 1000
-        filename = "imagenet-1k-id2label.json"
-        expected_shape = (1, 1000)
-    else:
-        num_labels = 21841
-        filename = "imagenet-22k-id2label.json"
-        expected_shape = (1, 21841)
-
-    repo_id = "huggingface/label-files"
-    config.num_labels = num_labels
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-    if "1k" not in checkpoint_url:
-        # this dataset contains 21843 labels but the model only has 21841
-        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
-        del id2label[9205]
-        del id2label[15027]
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-    config.hidden_sizes = hidden_sizes
-    config.depths = depths
-
-    return config, expected_shape
-
-
-def rename_key(name):
-    if "downsample_layers.0.0" in name:
-        name = name.replace("downsample_layers.0.0", "embeddings.patch_embeddings")
-    if "downsample_layers.0.1" in name:
-        name = name.replace("downsample_layers.0.1", "embeddings.norm")  # we rename to layernorm later on
-    if "downsample_layers.1.0" in name:
-        name = name.replace("downsample_layers.1.0", "stages.1.downsampling_layer.0")
-    if "downsample_layers.1.1" in name:
-        name = name.replace("downsample_layers.1.1", "stages.1.downsampling_layer.1")
-    if "downsample_layers.2.0" in name:
-        name = name.replace("downsample_layers.2.0", "stages.2.downsampling_layer.0")
-    if "downsample_layers.2.1" in name:
-        name = name.replace("downsample_layers.2.1", "stages.2.downsampling_layer.1")
-    if "downsample_layers.3.0" in name:
-        name = name.replace("downsample_layers.3.0", "stages.3.downsampling_layer.0")
-    if "downsample_layers.3.1" in name:
-        name = name.replace("downsample_layers.3.1", "stages.3.downsampling_layer.1")
-    if "stages" in name and "downsampling_layer" not in name:
-        # stages.0.0. for instance should be renamed to stages.0.layers.0.
-        name = name[: len("stages.0")] + ".layers" + name[len("stages.0") :]
-    if "stages" in name:
-        name = name.replace("stages", "encoder.stages")
-    if "norm" in name:
-        name = name.replace("norm", "layernorm")
-    if "gamma" in name:
-        name = name.replace("gamma", "layer_scale_parameter")
-    if "head" in name:
-        name = name.replace("head", "classifier")
-
-    return name
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_convnext_checkpoint(checkpoint_url, pytorch_dump_folder_path):
-    """
-    Copy/paste/tweak model's weights to our ConvNext structure.
-    """
-
-    # define ConvNext configuration based on URL
-    config, expected_shape = get_convnext_config(checkpoint_url)
-    # load original state_dict from URL
-    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]
-    # rename keys
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        state_dict[rename_key(key)] = val
-    # add prefix to all keys expect classifier head
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        if not key.startswith("classifier"):
-            key = "convnext." + key
-        state_dict[key] = val
-
-    # load HuggingFace model
-    model = ConvNextForImageClassification(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-
-    # Check outputs on an image, prepared by ConvNextImageProcessor
-    size = 224 if "224" in checkpoint_url else 384
-    image_processor = ConvNextImageProcessor(size=size)
-    pixel_values = image_processor(images=prepare_img(), return_tensors="pt").pixel_values
-
-    logits = model(pixel_values).logits
-
-    # note: the logits below were obtained without center cropping
-    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
-        expected_logits = torch.tensor([-0.1210, -0.6605, 0.1918])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
-        expected_logits = torch.tensor([-0.4473, -0.1847, -0.6365])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
-        expected_logits = torch.tensor([0.4525, 0.7539, 0.0308])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
-        expected_logits = torch.tensor([0.3561, 0.6350, -0.0384])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
-        expected_logits = torch.tensor([0.4174, -0.0989, 0.1489])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
-        expected_logits = torch.tensor([0.2513, -0.1349, -0.1613])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
-        expected_logits = torch.tensor([1.2980, 0.3631, -0.1198])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
-        expected_logits = torch.tensor([1.2963, 0.1227, 0.1723])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
-        expected_logits = torch.tensor([1.7956, 0.8390, 0.2820])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
-        expected_logits = torch.tensor([-0.2822, -0.0502, -0.0878])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
-        expected_logits = torch.tensor([-0.5672, -0.0730, -0.4348])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
-        expected_logits = torch.tensor([0.2681, 0.2365, 0.6246])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
-        expected_logits = torch.tensor([-0.2642, 0.3931, 0.5116])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
-        expected_logits = torch.tensor([-0.6677, -0.1873, -0.8379])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
-        expected_logits = torch.tensor([-0.7749, -0.2967, -0.6444])
-    else:
-        raise ValueError(f"Unknown URL: {checkpoint_url}")
-
-    assert torch.allclose(logits[0, :3], expected_logits, atol=1e-3)
-    assert logits.shape == expected_shape
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    print(f"Saving model to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Saving image processor to {pytorch_dump_folder_path}")
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-    print("Pushing model to the hub...")
-    model_name = "convnext"
-    if "tiny" in checkpoint_url:
-        model_name += "-tiny"
-    elif "small" in checkpoint_url:
-        model_name += "-small"
-    elif "base" in checkpoint_url:
-        model_name += "-base"
-    elif "xlarge" in checkpoint_url:
-        model_name += "-xlarge"
-    elif "large" in checkpoint_url:
-        model_name += "-large"
-    if "224" in checkpoint_url:
-        model_name += "-224"
-    elif "384" in checkpoint_url:
-        model_name += "-384"
-    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
-        model_name += "-22k"
-    if "22k" in checkpoint_url and "1k" in checkpoint_url:
-        model_name += "-22k-1k"
-
-    model.push_to_hub(
-        repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
-        organization="nielsr",
-        commit_message="Add model",
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--checkpoint_url",
-        default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
-        type=str,
-        help="URL of the original ConvNeXT checkpoint you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the output PyTorch model directory.",
-    )
-
-    args = parser.parse_args()
-    convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

src/transformers/models/convnextv2/convert_convnextv2_to_pytorch.py

@@ -1,286 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert ConvNeXTV2 checkpoints from the original repository.
-
-URL: https://github.com/facebookresearch/ConvNeXt"""
-
-import argparse
-import json
-import os
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import ConvNextImageProcessor, ConvNextV2Config, ConvNextV2ForImageClassification
-from transformers.image_utils import PILImageResampling
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_convnextv2_config(checkpoint_url):
-    config = ConvNextV2Config()
-
-    if "atto" in checkpoint_url:
-        depths = [2, 2, 6, 2]
-        hidden_sizes = [40, 80, 160, 320]
-    if "femto" in checkpoint_url:
-        depths = [2, 2, 6, 2]
-        hidden_sizes = [48, 96, 192, 384]
-    if "pico" in checkpoint_url:
-        depths = [2, 2, 6, 2]
-        hidden_sizes = [64, 128, 256, 512]
-    if "nano" in checkpoint_url:
-        depths = [2, 2, 8, 2]
-        hidden_sizes = [80, 160, 320, 640]
-    if "tiny" in checkpoint_url:
-        depths = [3, 3, 9, 3]
-        hidden_sizes = [96, 192, 384, 768]
-    if "base" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [128, 256, 512, 1024]
-    if "large" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [192, 384, 768, 1536]
-    if "huge" in checkpoint_url:
-        depths = [3, 3, 27, 3]
-        hidden_sizes = [352, 704, 1408, 2816]
-
-    num_labels = 1000
-    filename = "imagenet-1k-id2label.json"
-    expected_shape = (1, 1000)
-
-    repo_id = "huggingface/label-files"
-    config.num_labels = num_labels
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-    config.hidden_sizes = hidden_sizes
-    config.depths = depths
-
-    return config, expected_shape
-
-
-def rename_key(name):
-    if "downsample_layers.0.0" in name:
-        name = name.replace("downsample_layers.0.0", "embeddings.patch_embeddings")
-    if "downsample_layers.0.1" in name:
-        name = name.replace("downsample_layers.0.1", "embeddings.norm")  # we rename to layernorm later on
-    if "downsample_layers.1.0" in name:
-        name = name.replace("downsample_layers.1.0", "stages.1.downsampling_layer.0")
-    if "downsample_layers.1.1" in name:
-        name = name.replace("downsample_layers.1.1", "stages.1.downsampling_layer.1")
-    if "downsample_layers.2.0" in name:
-        name = name.replace("downsample_layers.2.0", "stages.2.downsampling_layer.0")
-    if "downsample_layers.2.1" in name:
-        name = name.replace("downsample_layers.2.1", "stages.2.downsampling_layer.1")
-    if "downsample_layers.3.0" in name:
-        name = name.replace("downsample_layers.3.0", "stages.3.downsampling_layer.0")
-    if "downsample_layers.3.1" in name:
-        name = name.replace("downsample_layers.3.1", "stages.3.downsampling_layer.1")
-    if "stages" in name and "downsampling_layer" not in name:
-        # stages.0.0. for instance should be renamed to stages.0.layers.0.
-        name = name[: len("stages.0")] + ".layers" + name[len("stages.0") :]
-    if "gamma" in name:
-        name = name.replace("gamma", "weight")
-    if "beta" in name:
-        name = name.replace("beta", "bias")
-    if "stages" in name:
-        name = name.replace("stages", "encoder.stages")
-    if "norm" in name:
-        name = name.replace("norm", "layernorm")
-    if "head" in name:
-        name = name.replace("head", "classifier")
-
-    return name
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-def convert_preprocessor(checkpoint_url):
-    if "224" in checkpoint_url:
-        size = 224
-        crop_pct = 224 / 256
-    elif "384" in checkpoint_url:
-        size = 384
-        crop_pct = None
-    else:
-        size = 512
-        crop_pct = None
-
-    return ConvNextImageProcessor(
-        size=size,
-        crop_pct=crop_pct,
-        image_mean=[0.485, 0.456, 0.406],
-        image_std=[0.229, 0.224, 0.225],
-        resample=PILImageResampling.BICUBIC,
-    )
-
-
-@torch.no_grad()
-def convert_convnextv2_checkpoint(checkpoint_url, pytorch_dump_folder_path, save_model, push_to_hub):
-    """
-    Copy/paste/tweak model's weights to our ConvNeXTV2 structure.
-    """
-    print("Downloading original model from checkpoint...")
-    # define ConvNeXTV2 configuration based on URL
-    config, expected_shape = get_convnextv2_config(checkpoint_url)
-    # load original state_dict from URL
-    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]
-
-    print("Converting model parameters...")
-    # rename keys
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        state_dict[rename_key(key)] = val
-    # add prefix to all keys expect classifier head
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        if not key.startswith("classifier"):
-            key = "convnextv2." + key
-        state_dict[key] = val
-
-    # load HuggingFace model
-    model = ConvNextV2ForImageClassification(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-
-    # Check outputs on an image, prepared by ConvNextImageProcessor
-    preprocessor = convert_preprocessor(checkpoint_url)
-    inputs = preprocessor(images=prepare_img(), return_tensors="pt")
-    logits = model(**inputs).logits
-
-    # note: the logits below were obtained without center cropping
-    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
-        expected_logits = torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
-        expected_logits = torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
-        expected_logits = torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
-        expected_logits = torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
-        expected_logits = torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
-        expected_logits = torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
-        expected_logits = torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
-        expected_logits = torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
-        expected_logits = torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
-        expected_logits = torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
-        expected_logits = torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309])
-    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
-        expected_logits = torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826])
-    else:
-        raise ValueError(f"Unknown URL: {checkpoint_url}")
-
-    assert torch.allclose(logits[0, :5], expected_logits, atol=1e-3)
-    assert logits.shape == expected_shape
-    print("Model outputs match the original results!")
-
-    if save_model:
-        print("Saving model to local...")
-        # Create folder to save model
-        if not os.path.isdir(pytorch_dump_folder_path):
-            os.mkdir(pytorch_dump_folder_path)
-
-        model.save_pretrained(pytorch_dump_folder_path)
-        preprocessor.save_pretrained(pytorch_dump_folder_path)
-
-    model_name = "convnextv2"
-    if "atto" in checkpoint_url:
-        model_name += "-atto"
-    if "femto" in checkpoint_url:
-        model_name += "-femto"
-    if "pico" in checkpoint_url:
-        model_name += "-pico"
-    if "nano" in checkpoint_url:
-        model_name += "-nano"
-    elif "tiny" in checkpoint_url:
-        model_name += "-tiny"
-    elif "base" in checkpoint_url:
-        model_name += "-base"
-    elif "large" in checkpoint_url:
-        model_name += "-large"
-    elif "huge" in checkpoint_url:
-        model_name += "-huge"
-    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
-        model_name += "-22k"
-    elif "22k" in checkpoint_url and "1k" in checkpoint_url:
-        model_name += "-22k-1k"
-    elif "1k" in checkpoint_url:
-        model_name += "-1k"
-    if "224" in checkpoint_url:
-        model_name += "-224"
-    elif "384" in checkpoint_url:
-        model_name += "-384"
-    elif "512" in checkpoint_url:
-        model_name += "-512"
-
-    if push_to_hub:
-        print(f"Pushing {model_name} to the hub...")
-        model.push_to_hub(model_name)
-        preprocessor.push_to_hub(model_name)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--checkpoint_url",
-        default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
-        type=str,
-        help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default="model",
-        type=str,
-        help="Path to the output PyTorch model directory.",
-    )
-    parser.add_argument("--save_model", action="store_true", help="Save model to local")
-    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")
-
-    args = parser.parse_args()
-    convert_convnextv2_checkpoint(
-        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
-    )

src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py

@@ -1,362 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert CvT checkpoints from the original repository.
-
-URL: https://github.com/microsoft/CvT"""
-
-import argparse
-import json
-from collections import OrderedDict
-from pathlib import Path
-
-import torch
-from huggingface_hub import hf_hub_download
-
-from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
-
-
-def embeddings(idx):
-    """
-    The function helps in renaming embedding layer weights.
-
-    Args:
-        idx: stage number in original model
-    """
-    embed = []
-    embed.append(
-        (
-            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
-            f"stage{idx}.patch_embed.proj.weight",
-        )
-    )
-    embed.append(
-        (
-            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
-            f"stage{idx}.patch_embed.proj.bias",
-        )
-    )
-    embed.append(
-        (
-            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
-            f"stage{idx}.patch_embed.norm.weight",
-        )
-    )
-    embed.append(
-        (
-            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
-            f"stage{idx}.patch_embed.norm.bias",
-        )
-    )
-    return embed
-
-
-def attention(idx, cnt):
-    """
-    The function helps in renaming attention block layers weights.
-
-    Args:
-        idx: stage number in original model
-        cnt: count of blocks in each stage
-    """
-    attention_weights = []
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
-            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
-            f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
-            f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
-            f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
-            f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
-            f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
-            f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
-            f"stage{idx}.blocks.{cnt}.attn.proj.weight",
-        )
-    )
-    attention_weights.append(
-        (
-            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
-            f"stage{idx}.blocks.{cnt}.attn.proj.bias",
-        )
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")
-    )
-    attention_weights.append(
-        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")
-    )
-    return attention_weights
-
-
-def cls_token(idx):
-    """
-    Function helps in renaming cls_token weights
-    """
-    token = []
-    token.append((f"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token"))
-    return token
-
-
-def final():
-    """
-    Function helps in renaming final classification layer
-    """
-    head = []
-    head.append(("layernorm.weight", "norm.weight"))
-    head.append(("layernorm.bias", "norm.bias"))
-    head.append(("classifier.weight", "head.weight"))
-    head.append(("classifier.bias", "head.bias"))
-    return head
-
-
-def convert_cvt_checkpoint(cvt_model, image_size, cvt_file_name, pytorch_dump_folder):
-    """
-    Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
-    """
-    img_labels_file = "imagenet-1k-id2label.json"
-    num_labels = 1000
-
-    repo_id = "huggingface/label-files"
-    num_labels = num_labels
-    id2label = json.loads(Path(hf_hub_download(repo_id, img_labels_file, repo_type="dataset")).read_text())
-    id2label = {int(k): v for k, v in id2label.items()}
-
-    id2label = id2label
-    label2id = {v: k for k, v in id2label.items()}
-
-    config = config = CvtConfig(num_labels=num_labels, id2label=id2label, label2id=label2id)
-
-    # For depth size 13 (13 = 1+2+10)
-    if cvt_model.rsplit("/", 1)[-1][4:6] == "13":
-        config.depth = [1, 2, 10]
-
-    # For depth size 21 (21 = 1+4+16)
-    elif cvt_model.rsplit("/", 1)[-1][4:6] == "21":
-        config.depth = [1, 4, 16]
-
-    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
-    else:
-        config.depth = [2, 2, 20]
-        config.num_heads = [3, 12, 16]
-        config.embed_dim = [192, 768, 1024]
-
-    model = CvtForImageClassification(config)
-    image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
-    image_processor.size["shortest_edge"] = image_size
-    original_weights = torch.load(cvt_file_name, map_location=torch.device("cpu"))
-
-    huggingface_weights = OrderedDict()
-    list_of_state_dict = []
-
-    for idx in range(len(config.depth)):
-        if config.cls_token[idx]:
-            list_of_state_dict = list_of_state_dict + cls_token(idx)
-        list_of_state_dict = list_of_state_dict + embeddings(idx)
-        for cnt in range(config.depth[idx]):
-            list_of_state_dict = list_of_state_dict + attention(idx, cnt)
-
-    list_of_state_dict = list_of_state_dict + final()
-    for gg in list_of_state_dict:
-        print(gg)
-    for i in range(len(list_of_state_dict)):
-        huggingface_weights[list_of_state_dict[i][0]] = original_weights[list_of_state_dict[i][1]]
-
-    model.load_state_dict(huggingface_weights)
-    model.save_pretrained(pytorch_dump_folder)
-    image_processor.save_pretrained(pytorch_dump_folder)
-
-
-# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--cvt_model",
-        default="cvt-w24",
-        type=str,
-        help="Name of the cvt model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--image_size",
-        default=384,
-        type=int,
-        help="Input Image Size",
-    )
-    parser.add_argument(
-        "--cvt_file_name",
-        default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
-        type=str,
-        help="Input Image Size",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
-    )
-
-    args = parser.parse_args()
-    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

src/transformers/models/dab_detr/convert_dab_detr_original_pytorch_checkpoint_to_pytorch.py

@@ -1,233 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert DAB-DETR checkpoints."""
-
-import argparse
-import gc
-import json
-import re
-from pathlib import Path
-
-import torch
-from huggingface_hub import hf_hub_download
-
-from transformers import ConditionalDetrImageProcessor, DabDetrConfig, DabDetrForObjectDetection
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
-    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
-    # for dab-DETR, also convert reference point head and query scale MLP
-    r"input_proj\.(bias|weight)": r"input_projection.\1",
-    r"refpoint_embed\.weight": r"query_refpoint_embeddings.weight",
-    r"class_embed\.(bias|weight)": r"class_embed.\1",
-    # negative lookbehind because of the overlap
-    r"(?<!transformer\.decoder\.)bbox_embed\.layers\.(\d+)\.(bias|weight)": r"bbox_predictor.layers.\1.\2",
-    r"transformer\.encoder\.query_scale\.layers\.(\d+)\.(bias|weight)": r"encoder.query_scale.layers.\1.\2",
-    r"transformer\.decoder\.bbox_embed\.layers\.(\d+)\.(bias|weight)": r"decoder.bbox_embed.layers.\1.\2",
-    r"transformer\.decoder\.norm\.(bias|weight)": r"decoder.layernorm.\1",
-    r"transformer\.decoder\.ref_point_head\.layers\.(\d+)\.(bias|weight)": r"decoder.ref_point_head.layers.\1.\2",
-    r"transformer\.decoder\.ref_anchor_head\.layers\.(\d+)\.(bias|weight)": r"decoder.ref_anchor_head.layers.\1.\2",
-    r"transformer\.decoder\.query_scale\.layers\.(\d+)\.(bias|weight)": r"decoder.query_scale.layers.\1.\2",
-    r"transformer\.decoder\.layers\.0\.ca_qpos_proj\.(bias|weight)": r"decoder.layers.0.cross_attn.cross_attn_query_pos_proj.\1",
-    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + activation function
-    # output projection
-    r"transformer\.encoder\.layers\.(\d+)\.self_attn\.out_proj\.(bias|weight)": r"encoder.layers.\1.self_attn.out_proj.\2",
-    # FFN layers
-    r"transformer\.encoder\.layers\.(\d+)\.linear(\d)\.(bias|weight)": r"encoder.layers.\1.fc\2.\3",
-    # normalization layers
-    # nm1
-    r"transformer\.encoder\.layers\.(\d+)\.norm1\.(bias|weight)": r"encoder.layers.\1.self_attn_layer_norm.\2",
-    # nm2
-    r"transformer\.encoder\.layers\.(\d+)\.norm2\.(bias|weight)": r"encoder.layers.\1.final_layer_norm.\2",
-    # activation function weight
-    r"transformer\.encoder\.layers\.(\d+)\.activation\.weight": r"encoder.layers.\1.activation_fn.weight",
-    #########################################################################################################################################
-    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms + activiation function weight
-    r"transformer\.decoder\.layers\.(\d+)\.self_attn\.out_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn.output_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.cross_attn\.out_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn.output_proj.\2",
-    # FFNs
-    r"transformer\.decoder\.layers\.(\d+)\.linear(\d)\.(bias|weight)": r"decoder.layers.\1.mlp.fc\2.\3",
-    # nm1
-    r"transformer\.decoder\.layers\.(\d+)\.norm1\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_layer_norm.\2",
-    # nm2
-    r"transformer\.decoder\.layers\.(\d+)\.norm2\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_layer_norm.\2",
-    # nm3
-    r"transformer\.decoder\.layers\.(\d+)\.norm3\.(bias|weight)": r"decoder.layers.\1.mlp.final_layer_norm.\2",
-    # activation function weight
-    r"transformer\.decoder\.layers\.(\d+)\.activation\.weight": r"decoder.layers.\1.mlp.activation_fn.weight",
-    # q, k, v projections and biases in self-attention in decoder
-    r"transformer\.decoder\.layers\.(\d+)\.sa_qcontent_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_query_content_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.sa_kcontent_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_key_content_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.sa_qpos_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_query_pos_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.sa_kpos_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_key_pos_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.sa_v_proj\.(bias|weight)": r"decoder.layers.\1.self_attn.self_attn_value_proj.\2",
-    # q, k, v projections in cross-attention in decoder
-    r"transformer\.decoder\.layers\.(\d+)\.ca_qcontent_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_query_content_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.ca_kcontent_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_key_content_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.ca_kpos_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_key_pos_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.ca_v_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_value_proj.\2",
-    r"transformer\.decoder\.layers\.(\d+)\.ca_qpos_sine_proj\.(bias|weight)": r"decoder.layers.\1.cross_attn.cross_attn_query_pos_sine_proj.\2",
-}
-
-
-# Copied from transformers.models.mllama.convert_mllama_weights_to_hf.convert_old_keys_to_new_keys
-def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
-    """
-    This function should be applied only once, on the concatenated keys to efficiently rename using
-    the key mappings.
-    """
-    output_dict = {}
-    if state_dict_keys is not None:
-        old_text = "\n".join(state_dict_keys)
-        new_text = old_text
-        for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
-            if replacement is None:
-                new_text = re.sub(pattern, "", new_text)  # an empty line
-                continue
-            new_text = re.sub(pattern, replacement, new_text)
-        output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
-    return output_dict
-
-
-def write_image_processor(model_name, pytorch_dump_folder_path, push_to_hub):
-    logger.info("Converting image processor...")
-    format = "coco_detection"
-    image_processor = ConditionalDetrImageProcessor(format=format)
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        image_processor.push_to_hub(repo_id=model_name, commit_message="Add new image processor")
-
-
-@torch.no_grad()
-def write_model(model_name, pretrained_model_weights_path, pytorch_dump_folder_path, push_to_hub):
-    # load modified config. Why? After loading the default config, the backbone kwargs are already set.
-    if "dc5" in model_name:
-        config = DabDetrConfig(dilation=True)
-    else:
-        # load default config
-        config = DabDetrConfig()
-    # set other attributes
-    if "dab-detr-resnet-50-dc5" == model_name:
-        config.temperature_height = 10
-        config.temperature_width = 10
-    if "fixxy" in model_name:
-        config.random_refpoints_xy = True
-    if "pat3" in model_name:
-        config.num_patterns = 3
-        # only when the number of patterns (num_patterns parameter in config) are more than 0 like r50-pat3 or r50dc5-pat3
-        ORIGINAL_TO_CONVERTED_KEY_MAPPING.update({r"transformer.patterns.weight": r"patterns.weight"})
-
-    config.num_labels = 91
-    repo_id = "huggingface/label-files"
-    filename = "coco-detection-id2label.json"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-    # load original model from local path
-    loaded = torch.load(pretrained_model_weights_path, map_location=torch.device("cpu"))["model"]
-    # Renaming the original model state dictionary to HF compatibile
-    all_keys = list(loaded.keys())
-    new_keys = convert_old_keys_to_new_keys(all_keys)
-    state_dict = {}
-    for key in all_keys:
-        if "backbone.0.body" in key:
-            new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model._backbone")
-            state_dict[new_key] = loaded[key]
-        # Q, K, V encoder values mapping
-        elif re.search("self_attn.in_proj_(weight|bias)", key):
-            # Dynamically find the layer number
-            pattern = r"layers\.(\d+)\.self_attn\.in_proj_(weight|bias)"
-            match = re.search(pattern, key)
-            if match:
-                layer_num = match.group(1)
-            else:
-                raise ValueError(f"Pattern not found in key: {key}")
-
-            in_proj_value = loaded.pop(key)
-            if "weight" in key:
-                state_dict[f"encoder.layers.{layer_num}.self_attn.q_proj.weight"] = in_proj_value[:256, :]
-                state_dict[f"encoder.layers.{layer_num}.self_attn.k_proj.weight"] = in_proj_value[256:512, :]
-                state_dict[f"encoder.layers.{layer_num}.self_attn.v_proj.weight"] = in_proj_value[-256:, :]
-            elif "bias" in key:
-                state_dict[f"encoder.layers.{layer_num}.self_attn.q_proj.bias"] = in_proj_value[:256]
-                state_dict[f"encoder.layers.{layer_num}.self_attn.k_proj.bias"] = in_proj_value[256:512]
-                state_dict[f"encoder.layers.{layer_num}.self_attn.v_proj.bias"] = in_proj_value[-256:]
-        else:
-            new_key = new_keys[key]
-            state_dict[new_key] = loaded[key]
-
-    del loaded
-    gc.collect()
-    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
-    prefix = "model."
-    for key in state_dict.copy().keys():
-        if not key.startswith("class_embed") and not key.startswith("bbox_predictor"):
-            val = state_dict.pop(key)
-            state_dict[prefix + key] = val
-    # finally, create HuggingFace model and load state dict
-    model = DabDetrForObjectDetection(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-    logger.info(f"Saving PyTorch model to {pytorch_dump_folder_path}...")
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-
-    if push_to_hub:
-        model.push_to_hub(repo_id=model_name, commit_message="Add new model")
-
-
-def convert_dab_detr_checkpoint(model_name, pretrained_model_weights_path, pytorch_dump_folder_path, push_to_hub):
-    logger.info("Converting image processor...")
-    write_image_processor(model_name, pytorch_dump_folder_path, push_to_hub)
-
-    logger.info(f"Converting model {model_name}...")
-    write_model(model_name, pretrained_model_weights_path, pytorch_dump_folder_path, push_to_hub)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_name",
-        default="dab-detr-resnet-50",
-        type=str,
-        help="Name of the DAB_DETR model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pretrained_model_weights_path",
-        default="modelzoo/R50/checkpoint.pth",
-        type=str,
-        help="The path of the original model weights like: modelzoo/checkpoint.pth",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default="DAB_DETR", type=str, help="Path to the folder to output PyTorch model."
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        default=True,
-        type=bool,
-        help="Whether to upload the converted weights and image processor config to the HuggingFace model profile. Default is set to false.",
-    )
-    args = parser.parse_args()
-    convert_dab_detr_checkpoint(
-        args.model_name, args.pretrained_model_weights_path, args.pytorch_dump_folder_path, args.push_to_hub
-    )

src/transformers/models/dac/convert_dac_checkpoint.py

@@ -1,261 +0,0 @@
-# coding=utf-8
-# Copyright 2024 Descript and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import fnmatch
-import re
-
-import torch
-
-from transformers import (
-    DacConfig,
-    DacFeatureExtractor,
-    DacModel,
-    logging,
-)
-
-
-# checkpoints downloaded using:
-# pip install descript-audio-codec
-# python3 -m dac download # downloads the default 44kHz variant
-# python3 -m dac download --model_type 44khz # downloads the 44kHz variant
-# python3 -m dac download --model_type 24khz # downloads the 24kHz variant
-# python3 -m dac download --model_type 16khz # downloads the 16kHz variant
-# More informations: https://github.com/descriptinc/descript-audio-codec/tree/main
-
-logging.set_verbosity_info()
-logger = logging.get_logger("transformers.models.dac")
-
-
-def match_pattern(string, pattern):
-    # Split the pattern into parts
-    pattern_parts = pattern.split(".")
-    string_parts = string.split(".")
-
-    pattern_block_count = string_block_count = 0
-
-    for part in pattern_parts:
-        if part.startswith("block"):
-            pattern_block_count += 1
-
-    for part in string_parts:
-        if part.startswith("block"):
-            string_block_count += 1
-
-    return fnmatch.fnmatch(string, pattern) and string_block_count == pattern_block_count
-
-
-TOP_LEVEL_KEYS = []
-IGNORE_KEYS = []
-
-
-MAPPING_ENCODER = {
-    "encoder.block.0": ["encoder.conv1"],
-    "encoder.block.5": ["encoder.snake1"],
-    "encoder.block.6": ["encoder.conv2"],
-    "encoder.block.*.block.*.block.0".replace("*", r"\d+"): ["encoder.block", "res_unit", "snake1"],
-    "encoder.block.*.block.*.block.1".replace("*", r"\d+"): ["encoder.block", "res_unit", "conv1"],
-    "encoder.block.*.block.*.block.2".replace("*", r"\d+"): ["encoder.block", "res_unit", "snake2"],
-    "encoder.block.*.block.*.block.3".replace("*", r"\d+"): ["encoder.block", "res_unit", "conv2"],
-    "encoder.block.*.block.3".replace("*", r"\d+"): ["encoder.block", "snake1"],
-    "encoder.block.*.block.4".replace("*", r"\d+"): ["encoder.block", "conv1"],
-}
-
-MAPPING_QUANTIZER = {
-    "quantizer.quantizers.*": ["quantizer.quantizers.*"],
-}
-
-MAPPING_DECODER = {
-    "decoder.model.0": ["decoder.conv1"],
-    "decoder.model.5": ["decoder.snake1"],
-    "decoder.model.6": ["decoder.conv2"],
-    "decoder.model.*.block.0".replace("*", r"\d+"): ["decoder.block", "snake1"],
-    "decoder.model.*.block.1".replace("*", r"\d+"): ["decoder.block", "conv_t1"],
-    "decoder.model.*.block.*.block.0".replace("*", r"\d+"): ["decoder.block", "res_unit", "snake1"],
-    "decoder.model.*.block.*.block.1".replace("*", r"\d+"): ["decoder.block", "res_unit", "conv1"],
-    "decoder.model.*.block.*.block.2".replace("*", r"\d+"): ["decoder.block", "res_unit", "snake2"],
-    "decoder.model.*.block.*.block.3".replace("*", r"\d+"): ["decoder.block", "res_unit", "conv2"],
-}
-
-
-MAPPING = {
-    **MAPPING_ENCODER,
-    **MAPPING_QUANTIZER,
-    **MAPPING_DECODER,
-}
-
-
-def set_recursively(hf_pointer, key, value, full_name, weight_type):
-    for attribute in key.split("."):
-        hf_pointer = getattr(hf_pointer, attribute)
-
-    if weight_type is not None:
-        hf_shape = getattr(hf_pointer, weight_type).shape
-    else:
-        hf_shape = hf_pointer.shape
-
-    if hf_shape != value.shape:
-        raise ValueError(
-            f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
-            f" {value.shape} for {full_name}"
-        )
-
-    if weight_type == "weight":
-        hf_pointer.weight.data = value
-    elif weight_type == "weight_g":
-        hf_pointer.weight_g.data = value
-    elif weight_type == "weight_v":
-        hf_pointer.weight_v.data = value
-    elif weight_type == "bias":
-        hf_pointer.bias.data = value
-    elif weight_type == "alpha":
-        hf_pointer.alpha.data = value
-    logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
-
-
-def should_ignore(name, ignore_keys):
-    for key in ignore_keys:
-        if key.endswith(".*"):
-            if name.startswith(key[:-1]):
-                return True
-        elif ".*." in key:
-            prefix, suffix = key.split(".*.")
-            if prefix in name and suffix in name:
-                return True
-        elif key in name:
-            return True
-    return False
-
-
-def recursively_load_weights(orig_dict, hf_model, model_name):
-    unused_weights = []
-
-    if model_name not in ["dac_16khz", "dac_24khz", "dac_44khz"]:
-        raise ValueError(f"Unsupported model: {model_name}")
-
-    for name, value in orig_dict.items():
-        is_used = False
-        for key, mapped_key in MAPPING.items():
-            regex = re.compile(key)
-            if regex.search(name):
-                if len(mapped_key) == 1:
-                    if mapped_key[0][0] == "q":
-                        mapped_key = ".".join(name.split(".")[:-1])
-                    else:
-                        mapped_key = mapped_key[0]
-                elif len(mapped_key) == 3:
-                    integers = re.findall(r"\b\d+\b", name)
-                    if mapped_key[0][0] == "d":
-                        mapped_key = "{}.{}.{}{}.{}".format(
-                            mapped_key[0],
-                            str(int(integers[0]) - 1),
-                            mapped_key[1],
-                            str(int(integers[1]) - 1),
-                            mapped_key[2],
-                        )
-                    else:
-                        mapped_key = "{}.{}.{}{}.{}".format(
-                            mapped_key[0],
-                            str(int(integers[0]) - 1),
-                            mapped_key[1],
-                            str(int(integers[1]) + 1),
-                            mapped_key[2],
-                        )
-                elif len(mapped_key) == 2:
-                    integers = re.findall(r"\b\d+\b", name)
-                    mapped_key = "{}.{}.{}".format(mapped_key[0], str(int(integers[0]) - 1), mapped_key[1])
-
-                is_used = True
-                if "weight_g" in name:
-                    weight_type = "weight_g"
-                elif "weight_v" in name:
-                    weight_type = "weight_v"
-                elif "bias" in name:
-                    weight_type = "bias"
-                elif "alpha" in name:
-                    weight_type = "alpha"
-                elif "weight" in name:
-                    weight_type = "weight"
-                set_recursively(hf_model, mapped_key, value, name, weight_type)
-
-        if not is_used:
-            unused_weights.append(name)
-
-    print(list(set(unused_weights)))
-
-    logger.warning(f"Unused weights: {unused_weights}")
-
-
-@torch.no_grad()
-def convert_checkpoint(
-    model_name,
-    checkpoint_path,
-    pytorch_dump_folder_path,
-    sample_rate=16000,
-    repo_id=None,
-):
-    model_dict = torch.load(checkpoint_path, "cpu")
-
-    config = DacConfig()
-
-    metadata = model_dict["metadata"]["kwargs"]
-    config.encoder_hidden_size = metadata["encoder_dim"]
-    config.downsampling_ratios = metadata["encoder_rates"]
-    config.codebook_size = metadata["codebook_size"]
-    config.n_codebooks = metadata["n_codebooks"]
-    config.codebook_dim = metadata["codebook_dim"]
-    config.decoder_hidden_size = metadata["decoder_dim"]
-    config.upsampling_ratios = metadata["decoder_rates"]
-    config.quantizer_dropout = float(metadata["quantizer_dropout"])
-    config.sampling_rate = sample_rate
-
-    model = DacModel(config)
-    feature_extractor = DacFeatureExtractor()
-    feature_extractor.sampling_rate = sample_rate
-
-    original_checkpoint = model_dict["state_dict"]
-
-    model.apply_weight_norm()
-    recursively_load_weights(original_checkpoint, model, model_name)
-    model.remove_weight_norm()
-
-    model.save_pretrained(pytorch_dump_folder_path)
-
-    if repo_id:
-        print("Pushing to the hub...")
-        feature_extractor.push_to_hub(repo_id)
-        model.push_to_hub(repo_id)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model",
-        default="dac_44khz",
-        type=str,
-        help="The model to convert. Should be one of 'dac_16khz', 'dac_24khz', 'dac_44khz'.",
-    )
-    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
-    parser.add_argument(
-        "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
-    )
-    parser.add_argument(
-        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
-    )
-    parser.add_argument("--sample_rate", default=None, type=str, help="Sample rate used by DacFeatureExtractor")
-    args = parser.parse_args()
-
-    convert_checkpoint(
-        args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.sample_rate, args.push_to_hub
-    )

src/transformers/models/data2vec/convert_data2vec_audio_original_pytorch_checkpoint_to_pytorch.py

@@ -1,285 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Wav2Vec2 checkpoint."""
-
-import argparse
-import os
-from functools import reduce
-
-import fairseq
-import torch
-from datasets import load_dataset
-
-from transformers import Wav2Vec2Processor, logging
-from transformers.models.data2vec.configuration_data2vec_audio import Data2VecAudioConfig
-
-# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
-from transformers.models.data2vec.data2vec_audio import Data2VecAudioModel as Dummy  # noqa: F401
-from transformers.models.data2vec.modeling_data2vec_audio import Data2VecAudioForCTC, Data2VecAudioModel
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-MAPPING = {
-    "post_extract_proj": "feature_projection.projection",
-    "models.0.layer_norm": "feature_projection.layer_norm",
-    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
-    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
-    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
-    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
-    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
-    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
-    "fc2": "encoder.layers.*.feed_forward.output_dense",
-    "final_layer_norm": "encoder.layers.*.final_layer_norm",
-    "encoder.layer_norm": "encoder.layer_norm",
-    "w2v_model.layer_norm": "feature_projection.layer_norm",
-    "w2v_encoder.proj": "lm_head",
-    "mask_emb": "masked_spec_embed",
-}
-TOP_LEVEL_KEYS = [
-    "lm_head",
-]
-
-
-def set_recursively(hf_pointer, key, value, full_name, weight_type):
-    for attribute in key.split("."):
-        hf_pointer = getattr(hf_pointer, attribute)
-
-    if weight_type is not None:
-        hf_shape = getattr(hf_pointer, weight_type).shape
-    else:
-        hf_shape = hf_pointer.shape
-
-    if hf_shape != value.shape:
-        raise ValueError(
-            f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
-            f" {value.shape} for {full_name}"
-        )
-
-    if weight_type == "weight":
-        hf_pointer.weight.data = value
-    elif weight_type == "weight_g":
-        hf_pointer.weight_g.data = value
-    elif weight_type == "weight_v":
-        hf_pointer.weight_v.data = value
-    elif weight_type == "bias":
-        hf_pointer.bias.data = value
-    else:
-        hf_pointer.data = value
-
-    logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
-
-
-def recursively_load_weights(fairseq_model, hf_model, is_headless):
-    unused_weights = []
-    fairseq_dict = fairseq_model.state_dict()
-
-    if not is_headless:
-        feature_extractor = hf_model.data2vec_audio.feature_extractor
-        pos_conv_embedding = hf_model.data2vec_audio.encoder.pos_conv_embed
-
-    else:
-        feature_extractor = hf_model.feature_extractor
-        pos_conv_embedding = hf_model.encoder.pos_conv_embed
-
-    for name, value in fairseq_dict.items():
-        is_used = False
-        if "conv_layers" in name:
-            load_conv_layer(
-                name,
-                value,
-                feature_extractor,
-                unused_weights,
-            )
-            is_used = True
-        elif "pos_conv" in name:
-            load_pos_conv_layer(
-                name,
-                value,
-                pos_conv_embedding,
-                unused_weights,
-            )
-            is_used = True
-        else:
-            for key, mapped_key in MAPPING.items():
-                if not is_headless:
-                    mapped_key = "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
-                if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
-                    is_used = True
-                    if "*" in mapped_key:
-                        layer_index = name.split(key)[0].split(".")[-2]
-                        mapped_key = mapped_key.replace("*", layer_index)
-                    if "weight_g" in name:
-                        weight_type = "weight_g"
-                    elif "weight_v" in name:
-                        weight_type = "weight_v"
-                    elif "bias" in name:
-                        weight_type = "bias"
-                    elif "weight" in name:
-                        # TODO: don't match quantizer.weight_proj
-                        weight_type = "weight"
-                    else:
-                        weight_type = None
-                    set_recursively(hf_model, mapped_key, value, name, weight_type)
-                continue
-        if not is_used:
-            unused_weights.append(name)
-
-    logger.warning(f"Unused weights: {unused_weights}")
-
-
-def access_by_string(module, path):
-    names = path.split(".")
-    return reduce(getattr, names, module)
-
-
-def set_weights(full_name, module, fsq_value, hf_weight_path):
-    hf_weight = access_by_string(module, hf_weight_path)
-    hf_value = hf_weight.data
-
-    if fsq_value.shape != hf_value.shape:
-        raise ValueError(f"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.")
-    hf_weight.data = fsq_value
-    logger.info(f"{full_name} was correctly initialized from {hf_weight_path}.")
-
-
-def load_conv_layer(full_name, value, feature_extractor, unused_weights):
-    name = full_name.split("conv_layers.")[-1]
-    items = name.split(".")
-    layer_id = int(items[0])
-    type_id = int(items[1])
-
-    weight_type = name.split(".")[-1]
-    if type_id == 0:
-        layer_type = "conv"
-    elif type_id == 2:
-        layer_type = "layer_norm"
-    else:
-        unused_weights.append(full_name)
-        return
-
-    set_weights(full_name, feature_extractor, value, f"conv_layers.{layer_id}.{layer_type}.{weight_type}")
-
-
-def load_pos_conv_layer(full_name, value, pos_conv_embeddings, unused_weights):
-    name = full_name.split("pos_conv.")[-1]
-    items = name.split(".")
-    layer_id = int(items[0])
-    type_id = int(items[1])
-
-    weight_type = name.split(".")[-1]
-    if type_id != 0:
-        unused_weights.append(full_name)
-        return
-    else:
-        layer_type = "conv"
-
-    set_weights(full_name, pos_conv_embeddings, value, f"layers.{layer_id}.{layer_type}.{weight_type}")
-
-
-@torch.no_grad()
-def convert_wav2vec2_checkpoint(
-    checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True
-):
-    """
-    Copy/paste/tweak model's weights to transformers design.
-    """
-    if config_path is not None:
-        config = Data2VecAudioConfig.from_pretrained(config_path)
-    else:
-        config = Data2VecAudioConfig()
-
-    if not is_finetuned:
-        # Modify final_proj layer name
-        hf_wav2vec = Data2VecAudioModel(config)
-        data2vec_checkpoint_dir = os.path.dirname(checkpoint_path)
-
-        state_dict = torch.load(checkpoint_path, weights_only=True)
-        state_dict["model"]["final_proj.weight"] = state_dict["model"].pop("final_proj.0.weight")
-        state_dict["model"]["final_proj.bias"] = state_dict["model"].pop("final_proj.0.bias")
-        converted_ckpt = os.path.join(data2vec_checkpoint_dir, "converted.pt")
-        torch.save(state_dict, converted_ckpt)
-    else:
-        hf_wav2vec = Data2VecAudioForCTC(config)
-        converted_ckpt = checkpoint_path
-
-    def load_data2vec(path):
-        model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([path])
-        return model[0].eval()
-
-    model = load_data2vec(converted_ckpt)
-
-    recursively_load_weights(model, hf_wav2vec, not is_finetuned)
-
-    processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-lv60")
-
-    ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
-    input_audio = [x["array"] for x in ds[:4]["audio"]]
-
-    inputs = processor(input_audio, return_tensors="pt", padding=True)
-
-    input_values = inputs.input_values
-    attention_mask = inputs.attention_mask
-    #    input_values = inputs.input_values[:, :-1]
-    #    attention_mask = inputs.attention_mask[:, :-1]
-
-    hf_wav2vec.eval()
-    model.eval()
-    if is_finetuned:
-        their_output = model(source=input_values, padding_mask=(1 - attention_mask), mask=False, features_only=True)[
-            "encoder_out"
-        ].transpose(0, 1)
-        our_output = hf_wav2vec(input_values, attention_mask=attention_mask)["logits"]
-
-        pred_ids = torch.argmax(our_output, dim=-1)
-        output_string = processor.batch_decode(pred_ids)
-
-        print(f"Expected Output: {ds[:4]['text']}, Pred: {output_string}")
-    else:
-        their_output = model(source=input_values, padding_mask=(1 - attention_mask), mask=False, features_only=True)[
-            "layer_results"
-        ][-1][0].transpose(0, 1)
-        our_output = hf_wav2vec(input_values, attention_mask=attention_mask)["last_hidden_state"]
-
-    print(our_output.shape, their_output.shape)
-    max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
-    print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
-    success = torch.allclose(our_output, their_output, atol=1e-3)
-    print("Do both models output the same tensors?", "🔥" if success else "💩")
-    if not success:
-        raise Exception("Something went wRoNg")
-
-    hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
-
-    if is_finetuned:
-        processor.save_pretrained(pytorch_dump_folder_path)
-    else:
-        processor.feature_extractor.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
-    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
-    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
-    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
-    parser.add_argument(
-        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
-    )
-    args = parser.parse_args()
-    convert_wav2vec2_checkpoint(
-        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
-    )

src/transformers/models/data2vec/convert_data2vec_text_original_pytorch_checkpoint_to_pytorch.py

@@ -1,207 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert data2vec checkpoint."""
-
-import argparse
-import os
-import pathlib
-
-import fairseq
-import torch
-from fairseq.modules import TransformerSentenceEncoderLayer
-from packaging import version
-
-from transformers import (
-    Data2VecTextConfig,
-    Data2VecTextForMaskedLM,
-    Data2VecTextForSequenceClassification,
-    Data2VecTextModel,
-)
-from transformers.models.bert.modeling_bert import (
-    BertIntermediate,
-    BertLayer,
-    BertOutput,
-    BertSelfAttention,
-    BertSelfOutput,
-)
-
-# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
-# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
-from transformers.utils import logging
-
-
-if version.parse(fairseq.__version__) < version.parse("0.9.0"):
-    raise Exception("requires fairseq >= 0.9.0")
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-SAMPLE_TEXT = "Hello world! cécé herlolip"
-
-
-def convert_data2vec_checkpoint_to_pytorch(
-    data2vec_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
-):
-    """
-    Copy/paste/tweak data2vec's weights to our BERT structure.
-    """
-    data2vec_checkpoint_dir, data2vec_checkpoint_file_name = os.path.split(data2vec_checkpoint_path)
-    data2vec = Data2VecTextModel.from_pretrained(
-        data2vec_checkpoint_dir, checkpoint_file=data2vec_checkpoint_file_name
-    )
-    data2vec.eval()  # disable dropout
-    data2vec_model = data2vec.models[0]
-    data2vec_sent_encoder = data2vec_model.encoder.sentence_encoder
-    config = Data2VecTextConfig(
-        vocab_size=data2vec_sent_encoder.embed_tokens.num_embeddings,
-        hidden_size=data2vec_model.args.encoder_embed_dim,
-        num_hidden_layers=data2vec_model.args.encoder_layers,
-        num_attention_heads=data2vec_model.args.encoder_attention_heads,
-        intermediate_size=data2vec_model.args.encoder_ffn_embed_dim,
-        max_position_embeddings=514,
-        type_vocab_size=1,
-        layer_norm_eps=1e-5,  # PyTorch default used in fairseq
-    )
-    if classification_head:
-        config.num_labels = data2vec.model.classification_heads["mnli"].out_proj.weight.shape[0]
-    print("Our BERT config:", config)
-
-    model = Data2VecTextForSequenceClassification(config) if classification_head else Data2VecTextForMaskedLM(config)
-    model.eval()
-
-    # Now let's copy all the weights.
-    # Embeddings
-    model.data2vec_text.embeddings.word_embeddings.weight = data2vec_sent_encoder.embed_tokens.weight
-    model.data2vec_text.embeddings.position_embeddings.weight = data2vec_sent_encoder.embed_positions.weight
-    model.data2vec_text.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
-        model.data2vec_text.embeddings.token_type_embeddings.weight
-    )  # just zero them out b/c data2vec doesn't use them.
-    model.data2vec_text.embeddings.LayerNorm.weight = data2vec_sent_encoder.layernorm_embedding.weight
-    model.data2vec_text.embeddings.LayerNorm.bias = data2vec_sent_encoder.layernorm_embedding.bias
-
-    for i in range(config.num_hidden_layers):
-        # Encoder: start of layer
-        layer: BertLayer = model.data2vec_text.encoder.layer[i]
-        data2vec_layer: TransformerSentenceEncoderLayer = data2vec_sent_encoder.layers[i]
-
-        # self attention
-        self_attn: BertSelfAttention = layer.attention.self
-        assert data2vec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
-            (config.hidden_size, config.hidden_size)
-        ), (
-            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
-            f" {torch.Size((config.hidden_size, config.hidden_size))}"
-        )
-        assert data2vec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
-            (config.hidden_size, config.hidden_size)
-        ), (
-            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
-            f" {torch.Size((config.hidden_size, config.hidden_size))}"
-        )
-        assert data2vec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
-            (config.hidden_size, config.hidden_size)
-        ), (
-            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
-            f" {torch.Size((config.hidden_size, config.hidden_size))}"
-        )
-
-        self_attn.query.weight.data = data2vec_layer.self_attn.q_proj.weight
-        self_attn.query.bias.data = data2vec_layer.self_attn.q_proj.bias
-        self_attn.key.weight.data = data2vec_layer.self_attn.k_proj.weight
-        self_attn.key.bias.data = data2vec_layer.self_attn.k_proj.bias
-        self_attn.value.weight.data = data2vec_layer.self_attn.v_proj.weight
-        self_attn.value.bias.data = data2vec_layer.self_attn.v_proj.bias
-
-        # self-attention output
-        self_output: BertSelfOutput = layer.attention.output
-        assert self_output.dense.weight.shape == data2vec_layer.self_attn.out_proj.weight.shape, (
-            f"Shape for self_output.dense.weight should be {data2vec_layer.self_attn.out_proj.weight.shape}"
-        )
-        self_output.dense.weight = data2vec_layer.self_attn.out_proj.weight
-        self_output.dense.bias = data2vec_layer.self_attn.out_proj.bias
-        self_output.LayerNorm.weight = data2vec_layer.self_attn_layer_norm.weight
-        self_output.LayerNorm.bias = data2vec_layer.self_attn_layer_norm.bias
-
-        # intermediate
-        intermediate: BertIntermediate = layer.intermediate
-        assert intermediate.dense.weight.shape == data2vec_layer.fc1.weight.shape, (
-            f"Shape for intermediate.dense.weight should be {data2vec_layer.fc1.weight.shape}"
-        )
-        intermediate.dense.weight = data2vec_layer.fc1.weight
-        intermediate.dense.bias = data2vec_layer.fc1.bias
-
-        # output
-        bert_output: BertOutput = layer.output
-        assert bert_output.dense.weight.shape == data2vec_layer.fc2.weight.shape, (
-            f"Shape for bert_output.dense.weight should be {data2vec_layer.fc2.weight.shape}"
-        )
-        bert_output.dense.weight = data2vec_layer.fc2.weight
-        bert_output.dense.bias = data2vec_layer.fc2.bias
-        bert_output.LayerNorm.weight = data2vec_layer.final_layer_norm.weight
-        bert_output.LayerNorm.bias = data2vec_layer.final_layer_norm.bias
-        # end of layer
-
-    if classification_head:
-        model.classifier.dense.weight = data2vec.model.classification_heads["mnli"].dense.weight
-        model.classifier.dense.bias = data2vec.model.classification_heads["mnli"].dense.bias
-        model.classifier.out_proj.weight = data2vec.model.classification_heads["mnli"].out_proj.weight
-        model.classifier.out_proj.bias = data2vec.model.classification_heads["mnli"].out_proj.bias
-    else:
-        # LM Head
-        model.lm_head.dense.weight = data2vec_model.encoder.lm_head.dense.weight
-        model.lm_head.dense.bias = data2vec_model.encoder.lm_head.dense.bias
-        model.lm_head.layer_norm.weight = data2vec_model.encoder.lm_head.layer_norm.weight
-        model.lm_head.layer_norm.bias = data2vec_model.encoder.lm_head.layer_norm.bias
-        model.lm_head.decoder.weight = data2vec_model.encoder.lm_head.weight
-        model.lm_head.decoder.bias = data2vec_model.encoder.lm_head.bias
-
-    # Let's check that we get the same results.
-    input_ids: torch.Tensor = data2vec.encode(SAMPLE_TEXT).unsqueeze(0)  # batch of size 1
-
-    our_output = model(input_ids)[0]
-    if classification_head:
-        their_output = data2vec.model.classification_heads["mnli"](data2vec.extract_features(input_ids))
-    else:
-        their_output = data2vec_model(input_ids)[0]
-    print(our_output.shape, their_output.shape)
-    max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
-    print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
-    success = torch.allclose(our_output, their_output, atol=1e-3)
-    print("Do both models output the same tensors?", "🔥" if success else "💩")
-    if not success:
-        raise Exception("Something went wRoNg")
-
-    pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
-    print(f"Saving model to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    parser.add_argument(
-        "--classification_head", action="store_true", help="Whether to convert a final classification head."
-    )
-    args = parser.parse_args()
-    convert_data2vec_checkpoint_to_pytorch(
-        args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
-    )

src/transformers/models/data2vec/convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py

@@ -1,374 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import json
-
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-from timm.models import create_model
-
-from transformers import (
-    BeitImageProcessor,
-    Data2VecVisionConfig,
-    Data2VecVisionForImageClassification,
-    Data2VecVisionModel,
-)
-
-
-def create_rename_keys(config, has_lm_head=False, is_semantic=False, hf_prefix="data2vec."):
-    prefix = "backbone." if is_semantic else ""
-
-    rename_keys = []
-    for i in range(config.num_hidden_layers):
-        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")
-        )
-        rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias"))
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")
-        )
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")
-        )
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")
-        )
-        rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias"))
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")
-        )
-        rename_keys.append(
-            (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")
-        )
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight"))
-        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias"))
-
-    # projection layer + position embeddings
-    rename_keys.extend(
-        [
-            (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
-            (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
-            (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
-        ]
-    )
-
-    if has_lm_head:
-        # mask token + shared relative position bias + layernorm
-        rename_keys.extend(
-            [
-                ("mask_token", f"{hf_prefix}embeddings.mask_token"),
-                (
-                    "rel_pos_bias.relative_position_bias_table",
-                    f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
-                ),
-                (
-                    "rel_pos_bias.relative_position_index",
-                    f"{hf_prefix}encoder.relative_position_bias.relative_position_index",
-                ),
-                ("norm.weight", "layernorm.weight"),
-                ("norm.bias", "layernorm.bias"),
-            ]
-        )
-    elif is_semantic:
-        # semantic segmentation classification heads
-        rename_keys.extend(
-            [
-                ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
-                ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
-                ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
-                ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
-            ]
-        )
-    else:
-        # layernorm + classification head
-        rename_keys.extend(
-            [
-                ("fc_norm.weight", f"{hf_prefix}pooler.layernorm.weight"),
-                ("fc_norm.bias", f"{hf_prefix}pooler.layernorm.bias"),
-                ("head.weight", "classifier.weight"),
-                ("head.bias", "classifier.bias"),
-            ]
-        )
-
-    return rename_keys
-
-
-def read_in_q_k_v(state_dict, config, has_lm_head=False, is_semantic=False, hf_prefix="data2vec_vision."):
-    for i in range(config.num_hidden_layers):
-        prefix = "backbone." if is_semantic else ""
-        # queries, keys and values
-        in_proj_weight = state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight")
-        q_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias")
-        v_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias")
-
-        state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
-            : config.hidden_size, :
-        ]
-        state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.query.bias"] = q_bias
-        state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
-            config.hidden_size : config.hidden_size * 2, :
-        ]
-        state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-            -config.hidden_size :, :
-        ]
-        state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.value.bias"] = v_bias
-
-        # gamma_1 and gamma_2
-        # we call them lambda because otherwise they are renamed when using .from_pretrained
-        gamma_1 = state_dict.pop(f"{prefix}blocks.{i}.gamma_1")
-        gamma_2 = state_dict.pop(f"{prefix}blocks.{i}.gamma_2")
-
-        state_dict[f"{hf_prefix}encoder.layer.{i}.lambda_1"] = gamma_1
-        state_dict[f"{hf_prefix}encoder.layer.{i}.lambda_2"] = gamma_2
-
-        # relative_position bias table + index
-        if not has_lm_head:
-            # each layer has its own relative position bias
-            table = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table")
-            index = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index")
-
-            state_dict[
-                f"{hf_prefix}encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table"
-            ] = table
-            state_dict[
-                f"{hf_prefix}encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index"
-            ] = index
-
-
-def get_args():
-    parser = argparse.ArgumentParser(
-        "Convert Data2VecVision to HF for image classification and pretraining", add_help=False
-    )
-    parser.add_argument("--hf_checkpoint_name", type=str)
-    parser.add_argument("--input_size", default=224, type=int, help="images input size")
-    parser.add_argument("--beit_checkpoint", default="", help="beit checkpoint")
-
-    return parser.parse_args()
-
-
-def load_beit_model(args, is_finetuned, is_large):
-    def load_state_dict(model, state_dict, prefix="", ignore_missing="relative_position_index"):
-        missing_keys = []
-        unexpected_keys = []
-        error_msgs = []
-        # copy state_dict so _load_from_state_dict can modify it
-        metadata = getattr(state_dict, "_metadata", None)
-        state_dict = state_dict.copy()
-        if metadata is not None:
-            state_dict._metadata = metadata
-
-        def load(module, prefix=""):
-            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
-            module._load_from_state_dict(
-                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
-            )
-            for name, child in module._modules.items():
-                if child is not None:
-                    load(child, prefix + name + ".")
-
-        load(model, prefix=prefix)
-
-        warn_missing_keys = []
-        ignore_missing_keys = []
-        for key in missing_keys:
-            keep_flag = True
-            for ignore_key in ignore_missing.split("|"):
-                if ignore_key in key:
-                    keep_flag = False
-                    break
-            if keep_flag:
-                warn_missing_keys.append(key)
-            else:
-                ignore_missing_keys.append(key)
-
-        missing_keys = warn_missing_keys
-
-        if len(missing_keys) > 0:
-            print(
-                "Weights of {} not initialized from pretrained model: {}".format(
-                    model.__class__.__name__, missing_keys
-                )
-            )
-        if len(unexpected_keys) > 0:
-            print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__, unexpected_keys))
-        if len(ignore_missing_keys) > 0:
-            print(
-                "Ignored weights of {} not initialized from pretrained model: {}".format(
-                    model.__class__.__name__, ignore_missing_keys
-                )
-            )
-        if len(error_msgs) > 0:
-            print("\n".join(error_msgs))
-
-    model_kwargs = {
-        "pretrained": False,
-        "use_shared_rel_pos_bias": True,
-        "use_abs_pos_emb": False,
-        "init_values": 0.1,
-    }
-
-    if is_finetuned:
-        model_kwargs.update(
-            {
-                "num_classes": 1000,
-                "use_mean_pooling": True,
-                "init_scale": 0.001,
-                "use_rel_pos_bias": True,
-            }
-        )
-
-    model = create_model(
-        "beit_large_patch16_224" if is_large else "beit_base_patch16_224",
-        **model_kwargs,
-    )
-    patch_size = model.patch_embed.patch_size
-    args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1])
-    checkpoint = torch.load(args.beit_checkpoint, map_location="cpu")
-
-    print(f"Load ckpt from {args.beit_checkpoint}")
-    checkpoint_model = None
-    for model_key in ("model", "module"):
-        if model_key in checkpoint:
-            checkpoint_model = checkpoint[model_key]
-            print(f"Load state_dict by model_key = {model_key}")
-            break
-
-    all_keys = list(checkpoint_model.keys())
-    for key in all_keys:
-        if "relative_position_index" in key:
-            checkpoint_model.pop(key)
-
-        if "relative_position_bias_table" in key:
-            rel_pos_bias = checkpoint_model[key]
-            src_num_pos, num_attn_heads = rel_pos_bias.size()
-            dst_num_pos, _ = model.state_dict()[key].size()
-            dst_patch_shape = model.patch_embed.patch_shape
-            if dst_patch_shape[0] != dst_patch_shape[1]:
-                raise NotImplementedError()
-
-    load_state_dict(model, checkpoint_model, prefix="")
-
-    return model
-
-
-def main():
-    args = get_args()
-
-    is_finetuned = "ft1k" in args.hf_checkpoint_name
-    is_large = "large" in args.hf_checkpoint_name
-
-    if is_finetuned:
-        # To convert Beit's data2vec_vision to HF you need to copy
-        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
-        # into this folder.
-        import modeling_finetune  # noqa: F401
-    else:
-        # To convert Beit's data2vec_vision to HF you need to copy
-        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
-        # into this folder
-        # IMPORTANT: Note that for now we've only converted the down-stream
-        # model and not the full pretrained model. This means for the integration
-        # test you need to add a `return x` after the following line:
-        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
-        # to make the integration test pass.
-        import modeling_cyclical  # noqa: F401
-
-    # 1. Create model config
-    config = Data2VecVisionConfig()
-    if is_finetuned:
-        config.use_relative_position_bias = True
-        config.use_shared_relative_position_bias = False
-        config.use_mean_pooling = True
-        config.num_labels = 1000
-
-        repo_id = "huggingface/label-files"
-        filename = "imagenet-1k-id2label.json"
-        id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-        id2label = {int(k): v for k, v in id2label.items()}
-        config.id2label = id2label
-        config.label2id = {v: k for k, v in id2label.items()}
-    else:
-        config.use_relative_position_bias = False
-        config.use_shared_relative_position_bias = True
-        config.use_mean_pooling = False
-
-    if is_large:
-        config.hidden_size = 1024
-        config.intermediate_size = 4096
-        config.num_hidden_layers = 24
-        config.num_attention_heads = 16
-
-    # 2. Load Beit model
-    orig_model = load_beit_model(args, is_finetuned, is_large)
-    orig_model.eval()
-
-    # 3. Forward Beit model
-    image_processor = BeitImageProcessor(size=config.image_size, do_center_crop=False)
-    image = Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png")
-    encoding = image_processor(images=image, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-
-    orig_args = (pixel_values,) if is_finetuned else (pixel_values, None)
-    with torch.no_grad():
-        orig_model_output = orig_model(*orig_args)
-
-    # 4. Load HF Data2VecVision model
-    if is_finetuned:
-        hf_model = Data2VecVisionForImageClassification(config)
-        hf_model.eval()
-        has_lm_head = False
-        hf_prefix = "data2vec_vision."
-    else:
-        hf_model = Data2VecVisionModel(config)
-        hf_model.eval()
-        has_lm_head = True
-        hf_prefix = ""
-
-    rename_keys = create_rename_keys(config, hf_prefix=hf_prefix, has_lm_head=has_lm_head)
-    state_dict = orig_model.state_dict()
-    for src, dest in rename_keys:
-        val = state_dict.pop(src)
-        state_dict[dest] = val
-
-    read_in_q_k_v(state_dict, config, hf_prefix=hf_prefix, has_lm_head=has_lm_head)
-    missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False)
-    print("HF missing", missing_keys)
-    print("HF unexpected_keys", unexpected_keys)
-
-    # 5. Forward HF Data2VecVision model
-    with torch.no_grad():
-        hf_model_output = hf_model(pixel_values)
-
-    hf_output = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
-
-    # 6. Compare
-    max_absolute_diff = torch.max(torch.abs(hf_output - orig_model_output)).item()
-
-    print(f"max_absolute_diff = {max_absolute_diff}")
-    success = torch.allclose(hf_output, orig_model_output, atol=1e-3)
-    print("Do both models output the same tensors?", "🔥" if success else "💩")
-    if not success:
-        raise Exception("Something went wRoNg")
-
-    # 7. Save
-    print(f"Saving to {args.hf_checkpoint_name}")
-    hf_model.save_pretrained(args.hf_checkpoint_name)
-    image_processor.save_pretrained(args.hf_checkpoint_name)
-
-
-if __name__ == "__main__":
-    main()
-    # Run the following to convert checkpoints
-    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
-    #          --beit_checkpoint ./pretrained_base.pt \
-    #          --hf_checkpoint_name "./data2vec-vision-base"
-    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
-    #          --beit_checkpoint ./finetuned_base.pt \
-    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
-    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
-    #          --beit_checkpoint ./pretrained_large.pt \
-    #          --hf_checkpoint_name "./data2vec-vision-large"
-    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
-    #          --beit_checkpoint ./finetuned_large.pt \
-    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

src/transformers/models/deformable_detr/convert_deformable_detr_to_pytorch.py

@@ -1,236 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Deformable DETR checkpoints."""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def rename_key(orig_key):
-    if "backbone.0.body" in orig_key:
-        orig_key = orig_key.replace("backbone.0.body", "backbone.conv_encoder.model")
-    if "transformer" in orig_key:
-        orig_key = orig_key.replace("transformer.", "")
-    if "norm1" in orig_key:
-        if "encoder" in orig_key:
-            orig_key = orig_key.replace("norm1", "self_attn_layer_norm")
-        else:
-            orig_key = orig_key.replace("norm1", "encoder_attn_layer_norm")
-    if "norm2" in orig_key:
-        if "encoder" in orig_key:
-            orig_key = orig_key.replace("norm2", "final_layer_norm")
-        else:
-            orig_key = orig_key.replace("norm2", "self_attn_layer_norm")
-    if "norm3" in orig_key:
-        orig_key = orig_key.replace("norm3", "final_layer_norm")
-    if "linear1" in orig_key:
-        orig_key = orig_key.replace("linear1", "fc1")
-    if "linear2" in orig_key:
-        orig_key = orig_key.replace("linear2", "fc2")
-    if "query_embed" in orig_key:
-        orig_key = orig_key.replace("query_embed", "query_position_embeddings")
-    if "cross_attn" in orig_key:
-        orig_key = orig_key.replace("cross_attn", "encoder_attn")
-
-    return orig_key
-
-
-def read_in_q_k_v(state_dict):
-    # transformer decoder self-attention layers
-    for i in range(6):
-        # read in weights + bias of input projection layer of self-attention
-        in_proj_weight = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight")
-        in_proj_bias = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_bias")
-        # next, add query, keys and values (in that order) to the state dict
-        state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
-        state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
-        state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
-        state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
-        state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
-        state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-
-    return im
-
-
-@torch.no_grad()
-def convert_deformable_detr_checkpoint(
-    checkpoint_path,
-    single_scale,
-    dilation,
-    with_box_refine,
-    two_stage,
-    pytorch_dump_folder_path,
-    push_to_hub,
-):
-    """
-    Copy/paste/tweak model's weights to our Deformable DETR structure.
-    """
-
-    # load default config
-    config = DeformableDetrConfig()
-    # set config attributes
-    if single_scale:
-        config.num_feature_levels = 1
-    config.dilation = dilation
-    config.with_box_refine = with_box_refine
-    config.two_stage = two_stage
-    # set labels
-    config.num_labels = 91
-    repo_id = "huggingface/label-files"
-    filename = "coco-detection-id2label.json"
-    id2label = json.loads(Path(hf_hub_download(repo_id, filename, repo_type="dataset")).read_text())
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    # load image processor
-    image_processor = DeformableDetrImageProcessor(format="coco_detection")
-
-    # prepare image
-    img = prepare_img()
-    encoding = image_processor(images=img, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-
-    logger.info("Converting model...")
-
-    # load original state dict
-    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-    # rename keys
-    for key in state_dict.copy().keys():
-        val = state_dict.pop(key)
-        state_dict[rename_key(key)] = val
-    # query, key and value matrices need special treatment
-    read_in_q_k_v(state_dict)
-    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
-    prefix = "model."
-    for key in state_dict.copy().keys():
-        if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
-            val = state_dict.pop(key)
-            state_dict[prefix + key] = val
-    # finally, create HuggingFace model and load state dict
-    model = DeformableDetrForObjectDetection(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-    # verify our conversion
-    outputs = model(pixel_values.to(device))
-
-    expected_logits = torch.tensor(
-        [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]
-    )
-    expected_boxes = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]])
-
-    if single_scale:
-        expected_logits = torch.tensor(
-            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]
-        )
-        expected_boxes = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]])
-
-    if single_scale and dilation:
-        expected_logits = torch.tensor(
-            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]
-        )
-        expected_boxes = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]])
-
-    if with_box_refine:
-        expected_logits = torch.tensor(
-            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]
-        )
-        expected_boxes = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]])
-
-    if with_box_refine and two_stage:
-        expected_logits = torch.tensor(
-            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]
-        )
-        expected_boxes = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]])
-
-    print("Logits:", outputs.logits[0, :3, :3])
-
-    assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
-    assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
-
-    print("Everything ok!")
-
-    # Save model and image processor
-    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_folder_path)
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-    # Push to hub
-    if push_to_hub:
-        model_name = "deformable-detr"
-        model_name += "-single-scale" if single_scale else ""
-        model_name += "-dc5" if dilation else ""
-        model_name += "-with-box-refine" if with_box_refine else ""
-        model_name += "-two-stage" if two_stage else ""
-        print("Pushing model to hub...")
-        model.push_to_hub(repo_path_or_name=model_name, organization="nielsr", commit_message="Add model")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--checkpoint_path",
-        type=str,
-        default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
-        help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
-    )
-    parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
-    parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
-    parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
-    parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the folder to output PyTorch model.",
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-    args = parser.parse_args()
-    convert_deformable_detr_checkpoint(
-        args.checkpoint_path,
-        args.single_scale,
-        args.dilation,
-        args.with_box_refine,
-        args.two_stage,
-        args.pytorch_dump_folder_path,
-        args.push_to_hub,
-    )

src/transformers/models/deit/convert_deit_timm_to_pytorch.py

@@ -1,218 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert DeiT distilled checkpoints from the timm library."""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import timm
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def create_rename_keys(config, base_model=False):
-    rename_keys = []
-    for i in range(config.num_hidden_layers):
-        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
-        rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight"))
-        rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias"))
-        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight"))
-        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias"))
-        rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight"))
-        rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias"))
-        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight"))
-        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias"))
-        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight"))
-        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias"))
-
-    # projection layer + position embeddings
-    rename_keys.extend(
-        [
-            ("cls_token", "deit.embeddings.cls_token"),
-            ("dist_token", "deit.embeddings.distillation_token"),
-            ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
-            ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
-            ("pos_embed", "deit.embeddings.position_embeddings"),
-        ]
-    )
-
-    if base_model:
-        # layernorm + pooler
-        rename_keys.extend(
-            [
-                ("norm.weight", "layernorm.weight"),
-                ("norm.bias", "layernorm.bias"),
-                ("pre_logits.fc.weight", "pooler.dense.weight"),
-                ("pre_logits.fc.bias", "pooler.dense.bias"),
-            ]
-        )
-
-        # if just the base model, we should remove "deit" from all keys that start with "deit"
-        rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("deit") else pair for pair in rename_keys]
-    else:
-        # layernorm + classification heads
-        rename_keys.extend(
-            [
-                ("norm.weight", "deit.layernorm.weight"),
-                ("norm.bias", "deit.layernorm.bias"),
-                ("head.weight", "cls_classifier.weight"),
-                ("head.bias", "cls_classifier.bias"),
-                ("head_dist.weight", "distillation_classifier.weight"),
-                ("head_dist.bias", "distillation_classifier.bias"),
-            ]
-        )
-
-    return rename_keys
-
-
-# we split up the matrix of each encoder layer into queries, keys and values
-def read_in_q_k_v(state_dict, config, base_model=False):
-    for i in range(config.num_hidden_layers):
-        if base_model:
-            prefix = ""
-        else:
-            prefix = "deit."
-        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
-        in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight")
-        in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias")
-        # next, add query, keys and values (in that order) to the state dict
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
-            : config.hidden_size, :
-        ]
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
-            config.hidden_size : config.hidden_size * 2, :
-        ]
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
-            config.hidden_size : config.hidden_size * 2
-        ]
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-            -config.hidden_size :, :
-        ]
-        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_deit_checkpoint(deit_name, pytorch_dump_folder_path):
-    """
-    Copy/paste/tweak model's weights to our DeiT structure.
-    """
-
-    # define default DeiT configuration
-    config = DeiTConfig()
-    # all deit models have fine-tuned heads
-    base_model = False
-    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
-    config.num_labels = 1000
-    repo_id = "huggingface/label-files"
-    filename = "imagenet-1k-id2label.json"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-    config.patch_size = int(deit_name[-6:-4])
-    config.image_size = int(deit_name[-3:])
-    # size of the architecture
-    if deit_name[9:].startswith("tiny"):
-        config.hidden_size = 192
-        config.intermediate_size = 768
-        config.num_hidden_layers = 12
-        config.num_attention_heads = 3
-    elif deit_name[9:].startswith("small"):
-        config.hidden_size = 384
-        config.intermediate_size = 1536
-        config.num_hidden_layers = 12
-        config.num_attention_heads = 6
-    if deit_name[9:].startswith("base"):
-        pass
-    elif deit_name[4:].startswith("large"):
-        config.hidden_size = 1024
-        config.intermediate_size = 4096
-        config.num_hidden_layers = 24
-        config.num_attention_heads = 16
-
-    # load original model from timm
-    timm_model = timm.create_model(deit_name, pretrained=True)
-    timm_model.eval()
-
-    # load state_dict of original model, remove and rename some keys
-    state_dict = timm_model.state_dict()
-    rename_keys = create_rename_keys(config, base_model)
-    for src, dest in rename_keys:
-        rename_key(state_dict, src, dest)
-    read_in_q_k_v(state_dict, config, base_model)
-
-    # load HuggingFace model
-    model = DeiTForImageClassificationWithTeacher(config).eval()
-    model.load_state_dict(state_dict)
-
-    # Check outputs on an image, prepared by DeiTImageProcessor
-    size = int(
-        (256 / 224) * config.image_size
-    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
-    image_processor = DeiTImageProcessor(size=size, crop_size=config.image_size)
-    encoding = image_processor(images=prepare_img(), return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-    outputs = model(pixel_values)
-
-    timm_logits = timm_model(pixel_values)
-    assert timm_logits.shape == outputs.logits.shape
-    assert torch.allclose(timm_logits, outputs.logits, atol=1e-3)
-
-    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-    print(f"Saving model {deit_name} to {pytorch_dump_folder_path}")
-    model.save_pretrained(pytorch_dump_folder_path)
-    print(f"Saving image processor to {pytorch_dump_folder_path}")
-    image_processor.save_pretrained(pytorch_dump_folder_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--deit_name",
-        default="vit_deit_base_distilled_patch16_224",
-        type=str,
-        help="Name of the DeiT timm model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
-    )
-
-    args = parser.parse_args()
-    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)

src/transformers/models/deprecated/bort/convert_bort_original_gluonnlp_checkpoint_to_pytorch.py

@@ -1,318 +0,0 @@
-# coding=utf-8
-# Copyright 2020, The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert Bort checkpoint."""
-
-import argparse
-import os
-
-import gluonnlp as nlp
-import mxnet as mx
-import numpy as np
-import torch
-from gluonnlp.base import get_home_dir
-from gluonnlp.model.bert import BERTEncoder
-from gluonnlp.model.utils import _load_vocab
-from gluonnlp.vocab import Vocab
-from packaging import version
-from torch import nn
-
-from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
-from transformers.models.bert.modeling_bert import (
-    BertIntermediate,
-    BertLayer,
-    BertOutput,
-    BertSelfAttention,
-    BertSelfOutput,
-)
-from transformers.utils import logging
-
-
-if version.parse(nlp.__version__) != version.parse("0.8.3"):
-    raise Exception("requires gluonnlp == 0.8.3")
-
-if version.parse(mx.__version__) != version.parse("1.5.0"):
-    raise Exception("requires mxnet == 1.5.0")
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-SAMPLE_TEXT = "The Nymphenburg Palace is a beautiful palace in Munich!"
-
-
-def convert_bort_checkpoint_to_pytorch(bort_checkpoint_path: str, pytorch_dump_folder_path: str):
-    """
-    Convert the original Bort checkpoint (based on MXNET and Gluonnlp) to our BERT structure-
-    """
-
-    # Original Bort configuration
-    bort_4_8_768_1024_hparams = {
-        "attention_cell": "multi_head",
-        "num_layers": 4,
-        "units": 1024,
-        "hidden_size": 768,
-        "max_length": 512,
-        "num_heads": 8,
-        "scaled": True,
-        "dropout": 0.1,
-        "use_residual": True,
-        "embed_size": 1024,
-        "embed_dropout": 0.1,
-        "word_embed": None,
-        "layer_norm_eps": 1e-5,
-        "token_type_vocab_size": 2,
-    }
-
-    predefined_args = bort_4_8_768_1024_hparams
-
-    # Let's construct the original Bort model here
-    # Taken from official BERT implementation, see:
-    # https://github.com/alexa/bort/blob/master/bort/bort.py
-    encoder = BERTEncoder(
-        attention_cell=predefined_args["attention_cell"],
-        num_layers=predefined_args["num_layers"],
-        units=predefined_args["units"],
-        hidden_size=predefined_args["hidden_size"],
-        max_length=predefined_args["max_length"],
-        num_heads=predefined_args["num_heads"],
-        scaled=predefined_args["scaled"],
-        dropout=predefined_args["dropout"],
-        output_attention=False,
-        output_all_encodings=False,
-        use_residual=predefined_args["use_residual"],
-        activation=predefined_args.get("activation", "gelu"),
-        layer_norm_eps=predefined_args.get("layer_norm_eps", None),
-    )
-
-    # Vocab information needs to be fetched first
-    # It's the same as RoBERTa, so RobertaTokenizer can be used later
-    vocab_name = "openwebtext_ccnews_stories_books_cased"
-
-    # Specify download folder to Gluonnlp's vocab
-    gluon_cache_dir = os.path.join(get_home_dir(), "models")
-    bort_vocab = _load_vocab(vocab_name, None, gluon_cache_dir, cls=Vocab)
-
-    original_bort = nlp.model.BERTModel(
-        encoder,
-        len(bort_vocab),
-        units=predefined_args["units"],
-        embed_size=predefined_args["embed_size"],
-        embed_dropout=predefined_args["embed_dropout"],
-        word_embed=predefined_args["word_embed"],
-        use_pooler=False,
-        use_token_type_embed=False,
-        token_type_vocab_size=predefined_args["token_type_vocab_size"],
-        use_classifier=False,
-        use_decoder=False,
-    )
-
-    original_bort.load_parameters(bort_checkpoint_path, cast_dtype=True, ignore_extra=True)
-    params = original_bort._collect_params_with_prefix()
-
-    # Build our config 🤗
-    hf_bort_config_json = {
-        "architectures": ["BertForMaskedLM"],
-        "attention_probs_dropout_prob": predefined_args["dropout"],
-        "hidden_act": "gelu",
-        "hidden_dropout_prob": predefined_args["dropout"],
-        "hidden_size": predefined_args["embed_size"],
-        "initializer_range": 0.02,
-        "intermediate_size": predefined_args["hidden_size"],
-        "layer_norm_eps": predefined_args["layer_norm_eps"],
-        "max_position_embeddings": predefined_args["max_length"],
-        "model_type": "bort",
-        "num_attention_heads": predefined_args["num_heads"],
-        "num_hidden_layers": predefined_args["num_layers"],
-        "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
-        "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
-        "vocab_size": len(bort_vocab),
-    }
-
-    hf_bort_config = BertConfig.from_dict(hf_bort_config_json)
-    hf_bort_model = BertForMaskedLM(hf_bort_config)
-    hf_bort_model.eval()
-
-    # Parameter mapping table (Gluonnlp to Transformers)
-    # * denotes layer index
-    #
-    # | Gluon Parameter                                                | Transformers Parameter
-    # | -------------------------------------------------------------- | ----------------------
-    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
-    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
-    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
-    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
-    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
-    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
-    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
-    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
-    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
-    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
-    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
-    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
-    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
-    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
-    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
-    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
-    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
-    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
-    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
-    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`
-
-    # Helper function to convert MXNET Arrays to PyTorch
-    def to_torch(mx_array) -> nn.Parameter:
-        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()))
-
-    # Check param shapes and map new HF param back
-    def check_and_map_params(hf_param, gluon_param):
-        shape_hf = hf_param.shape
-
-        gluon_param = to_torch(params[gluon_param])
-        shape_gluon = gluon_param.shape
-
-        assert shape_hf == shape_gluon, (
-            f"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"
-        )
-
-        return gluon_param
-
-    hf_bort_model.bert.embeddings.word_embeddings.weight = check_and_map_params(
-        hf_bort_model.bert.embeddings.word_embeddings.weight, "word_embed.0.weight"
-    )
-    hf_bort_model.bert.embeddings.position_embeddings.weight = check_and_map_params(
-        hf_bort_model.bert.embeddings.position_embeddings.weight, "encoder.position_weight"
-    )
-    hf_bort_model.bert.embeddings.LayerNorm.bias = check_and_map_params(
-        hf_bort_model.bert.embeddings.LayerNorm.bias, "encoder.layer_norm.beta"
-    )
-    hf_bort_model.bert.embeddings.LayerNorm.weight = check_and_map_params(
-        hf_bort_model.bert.embeddings.LayerNorm.weight, "encoder.layer_norm.gamma"
-    )
-
-    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
-    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
-        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data
-    )
-
-    for i in range(hf_bort_config.num_hidden_layers):
-        layer: BertLayer = hf_bort_model.bert.encoder.layer[i]
-
-        # self attention
-        self_attn: BertSelfAttention = layer.attention.self
-
-        self_attn.key.bias.data = check_and_map_params(
-            self_attn.key.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"
-        )
-
-        self_attn.key.weight.data = check_and_map_params(
-            self_attn.key.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"
-        )
-        self_attn.query.bias.data = check_and_map_params(
-            self_attn.query.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"
-        )
-        self_attn.query.weight.data = check_and_map_params(
-            self_attn.query.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"
-        )
-        self_attn.value.bias.data = check_and_map_params(
-            self_attn.value.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"
-        )
-        self_attn.value.weight.data = check_and_map_params(
-            self_attn.value.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"
-        )
-
-        # self attention output
-        self_output: BertSelfOutput = layer.attention.output
-
-        self_output.dense.bias = check_and_map_params(
-            self_output.dense.bias, f"encoder.transformer_cells.{i}.proj.bias"
-        )
-        self_output.dense.weight = check_and_map_params(
-            self_output.dense.weight, f"encoder.transformer_cells.{i}.proj.weight"
-        )
-        self_output.LayerNorm.bias = check_and_map_params(
-            self_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.layer_norm.beta"
-        )
-        self_output.LayerNorm.weight = check_and_map_params(
-            self_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.layer_norm.gamma"
-        )
-
-        # intermediate
-        intermediate: BertIntermediate = layer.intermediate
-
-        intermediate.dense.bias = check_and_map_params(
-            intermediate.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"
-        )
-        intermediate.dense.weight = check_and_map_params(
-            intermediate.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"
-        )
-
-        # output
-        bert_output: BertOutput = layer.output
-
-        bert_output.dense.bias = check_and_map_params(
-            bert_output.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_2.bias"
-        )
-        bert_output.dense.weight = check_and_map_params(
-            bert_output.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_2.weight"
-        )
-        bert_output.LayerNorm.bias = check_and_map_params(
-            bert_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.ffn.layer_norm.beta"
-        )
-        bert_output.LayerNorm.weight = check_and_map_params(
-            bert_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.ffn.layer_norm.gamma"
-        )
-
-    # Save space and energy 🎄
-    hf_bort_model.half()
-
-    # Compare output of both models
-    tokenizer = RobertaTokenizer.from_pretrained("FacebookAI/roberta-base")
-
-    input_ids = tokenizer.encode_plus(SAMPLE_TEXT)["input_ids"]
-
-    # Get gluon output
-    gluon_input_ids = mx.nd.array([input_ids])
-    output_gluon = original_bort(inputs=gluon_input_ids, token_types=[])
-
-    # Get Transformer output (save and reload model again)
-    hf_bort_model.save_pretrained(pytorch_dump_folder_path)
-    hf_bort_model = BertModel.from_pretrained(pytorch_dump_folder_path)
-    hf_bort_model.eval()
-
-    input_ids = tokenizer.encode_plus(SAMPLE_TEXT, return_tensors="pt")
-    output_hf = hf_bort_model(**input_ids)[0]
-
-    gluon_layer = output_gluon[0].asnumpy()
-    hf_layer = output_hf[0].detach().numpy()
-
-    max_absolute_diff = np.max(np.abs(hf_layer - gluon_layer)).item()
-    success = np.allclose(gluon_layer, hf_layer, atol=1e-3)
-
-    if success:
-        print("✔️ Both model do output the same tensors")
-    else:
-        print("❌ Both model do **NOT** output the same tensors")
-        print("Absolute difference is:", max_absolute_diff)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-    args = parser.parse_args()
-    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

src/transformers/models/deprecated/deta/convert_deta_resnet_to_pytorch.py

@@ -1,319 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert DETA checkpoints from the original repository.
-
-URL: https://github.com/jozhang97/DETA/tree/master"""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_deta_config():
-    config = DetaConfig(
-        num_queries=900,
-        encoder_ffn_dim=2048,
-        decoder_ffn_dim=2048,
-        num_feature_levels=5,
-        assign_first_stage=True,
-        with_box_refine=True,
-        two_stage=True,
-    )
-
-    # set labels
-    config.num_labels = 91
-    repo_id = "huggingface/label-files"
-    filename = "coco-detection-id2label.json"
-    id2label = json.loads(Path(hf_hub_download(repo_id, filename, repo_type="dataset")).read_text())
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    return config
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def create_rename_keys(config):
-    rename_keys = []
-
-    # stem
-    # fmt: off
-    rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight"))
-    rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight"))
-    rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias"))
-    rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean"))
-    rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var"))
-    # stages
-    for stage_idx in range(len(config.backbone_config.depths)):
-        for layer_idx in range(config.backbone_config.depths[stage_idx]):
-            # shortcut
-            if layer_idx == 0:
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
-                    )
-                )
-            # 3 convs
-            for i in range(3):
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
-                    )
-                )
-                rename_keys.append(
-                    (
-                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
-                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
-                    )
-                )
-    # transformer encoder
-    for i in range(config.encoder_layers):
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias"))
-
-    # transformer decoder
-    for i in range(config.decoder_layers):
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias"))
-
-    # fmt: on
-
-    return rename_keys
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-def read_in_decoder_q_k_v(state_dict, config):
-    # transformer decoder self-attention layers
-    hidden_size = config.d_model
-    for i in range(config.decoder_layers):
-        # read in weights + bias of input projection layer of self-attention
-        in_proj_weight = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight")
-        in_proj_bias = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias")
-        # next, add query, keys and values (in that order) to the state dict
-        state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:hidden_size, :]
-        state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:hidden_size]
-        state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[
-            hidden_size : hidden_size * 2, :
-        ]
-        state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
-        state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-hidden_size:, :]
-        state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-hidden_size:]
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-
-    return im
-
-
-@torch.no_grad()
-def convert_deta_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
-    """
-    Copy/paste/tweak model's weights to our DETA structure.
-    """
-
-    # load config
-    config = get_deta_config()
-
-    # load original state dict
-    if model_name == "deta-resnet-50":
-        filename = "adet_checkpoint0011.pth"
-    elif model_name == "deta-resnet-50-24-epochs":
-        filename = "adet_2x_checkpoint0023.pth"
-    else:
-        raise ValueError(f"Model name {model_name} not supported")
-    checkpoint_path = hf_hub_download(repo_id="nielsr/deta-checkpoints", filename=filename)
-    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-
-    # rename keys
-    rename_keys = create_rename_keys(config)
-    for src, dest in rename_keys:
-        rename_key(state_dict, src, dest)
-    read_in_decoder_q_k_v(state_dict, config)
-
-    # fix some prefixes
-    for key in state_dict.copy().keys():
-        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
-            val = state_dict.pop(key)
-            state_dict[key.replace("transformer.decoder", "model.decoder")] = val
-        if "input_proj" in key:
-            val = state_dict.pop(key)
-            state_dict["model." + key] = val
-        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
-            val = state_dict.pop(key)
-            state_dict[key.replace("transformer", "model")] = val
-
-    # finally, create HuggingFace model and load state dict
-    model = DetaForObjectDetection(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-
-    # load image processor
-    processor = DetaImageProcessor(format="coco_detection")
-
-    # verify our conversion on image
-    img = prepare_img()
-    encoding = processor(images=img, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-    outputs = model(pixel_values.to(device))
-
-    # verify logits
-    if model_name == "deta-resnet-50":
-        expected_logits = torch.tensor(
-            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]
-        )
-        expected_boxes = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]])
-    elif model_name == "deta-resnet-50-24-epochs":
-        expected_logits = torch.tensor(
-            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]
-        )
-        expected_boxes = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]])
-
-    assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
-    assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
-    print("Everything ok!")
-
-    if pytorch_dump_folder_path:
-        # Save model and processor
-        logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}...")
-        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-        model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    # Push to hub
-    if push_to_hub:
-        print("Pushing model and processor to hub...")
-        model.push_to_hub(f"jozhang97/{model_name}")
-        processor.push_to_hub(f"jozhang97/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_name",
-        type=str,
-        default="deta-resnet-50",
-        choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
-        help="Name of the model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        help="Path to the folder to output PyTorch model.",
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-    args = parser.parse_args()
-    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/deprecated/deta/convert_deta_swin_to_pytorch.py

@@ -1,326 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert DETA checkpoints from the original repository.
-
-URL: https://github.com/jozhang97/DETA/tree/master"""
-
-import argparse
-import json
-from pathlib import Path
-
-import requests
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-logger = logging.get_logger(__name__)
-
-
-def get_deta_config(model_name):
-    backbone_config = SwinConfig(
-        embed_dim=192,
-        depths=(2, 2, 18, 2),
-        num_heads=(6, 12, 24, 48),
-        window_size=12,
-        out_features=["stage2", "stage3", "stage4"],
-    )
-
-    config = DetaConfig(
-        backbone_config=backbone_config,
-        num_queries=900,
-        encoder_ffn_dim=2048,
-        decoder_ffn_dim=2048,
-        num_feature_levels=5,
-        assign_first_stage=True,
-        with_box_refine=True,
-        two_stage=True,
-    )
-
-    # set labels
-    repo_id = "huggingface/label-files"
-    if "o365" in model_name:
-        num_labels = 366
-        filename = "object365-id2label.json"
-    else:
-        num_labels = 91
-        filename = "coco-detection-id2label.json"
-
-    config.num_labels = num_labels
-    id2label = json.loads(Path(hf_hub_download(repo_id, filename, repo_type="dataset")).read_text())
-    id2label = {int(k): v for k, v in id2label.items()}
-    config.id2label = id2label
-    config.label2id = {v: k for k, v in id2label.items()}
-
-    return config
-
-
-# here we list all keys to be renamed (original name on the left, our name on the right)
-def create_rename_keys(config):
-    rename_keys = []
-
-    # stem
-    # fmt: off
-    rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight"))
-    rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias"))
-    rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight"))
-    rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias"))
-    # stages
-    for i in range(len(config.backbone_config.depths)):
-        for j in range(config.backbone_config.depths[i]):
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias"))
-
-        if i < 3:
-            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight"))
-            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias"))
-
-    rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight"))
-    rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias"))
-    rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight"))
-    rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias"))
-    rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight"))
-    rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias"))
-
-    # transformer encoder
-    for i in range(config.encoder_layers):
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight"))
-        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias"))
-
-    # transformer decoder
-    for i in range(config.decoder_layers):
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight"))
-        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias"))
-
-    # fmt: on
-
-    return rename_keys
-
-
-def rename_key(dct, old, new):
-    val = dct.pop(old)
-    dct[new] = val
-
-
-# we split up the matrix of each encoder layer into queries, keys and values
-def read_in_swin_q_k_v(state_dict, backbone_config):
-    num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
-    for i in range(len(backbone_config.depths)):
-        dim = num_features[i]
-        for j in range(backbone_config.depths[i]):
-            # fmt: off
-            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
-            in_proj_weight = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight")
-            in_proj_bias = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias")
-            # next, add query, keys and values (in that order) to the state dict
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim]
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
-                dim : dim * 2, :
-            ]
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[
-                dim : dim * 2
-            ]
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
-                -dim :, :
-            ]
-            state_dict[f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :]
-            # fmt: on
-
-
-def read_in_decoder_q_k_v(state_dict, config):
-    # transformer decoder self-attention layers
-    hidden_size = config.d_model
-    for i in range(config.decoder_layers):
-        # read in weights + bias of input projection layer of self-attention
-        in_proj_weight = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight")
-        in_proj_bias = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias")
-        # next, add query, keys and values (in that order) to the state dict
-        state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:hidden_size, :]
-        state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:hidden_size]
-        state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[
-            hidden_size : hidden_size * 2, :
-        ]
-        state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
-        state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-hidden_size:, :]
-        state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-hidden_size:]
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-
-    return im
-
-
-@torch.no_grad()
-def convert_deta_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
-    """
-    Copy/paste/tweak model's weights to our DETA structure.
-    """
-
-    # load config
-    config = get_deta_config(model_name)
-
-    # load original state dict
-    if model_name == "deta-swin-large":
-        checkpoint_path = hf_hub_download(repo_id="nielsr/deta-checkpoints", filename="adet_swin_ft.pth")
-    elif model_name == "deta-swin-large-o365":
-        checkpoint_path = hf_hub_download(repo_id="jozhang97/deta-swin-l-o365", filename="deta_swin_pt_o365.pth")
-    else:
-        raise ValueError(f"Model name {model_name} not supported")
-
-    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-
-    # original state dict
-    for name, param in state_dict.items():
-        print(name, param.shape)
-
-    # rename keys
-    rename_keys = create_rename_keys(config)
-    for src, dest in rename_keys:
-        rename_key(state_dict, src, dest)
-    read_in_swin_q_k_v(state_dict, config.backbone_config)
-    read_in_decoder_q_k_v(state_dict, config)
-
-    # fix some prefixes
-    for key in state_dict.copy().keys():
-        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
-            val = state_dict.pop(key)
-            state_dict[key.replace("transformer.decoder", "model.decoder")] = val
-        if "input_proj" in key:
-            val = state_dict.pop(key)
-            state_dict["model." + key] = val
-        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
-            val = state_dict.pop(key)
-            state_dict[key.replace("transformer", "model")] = val
-
-    # finally, create HuggingFace model and load state dict
-    model = DetaForObjectDetection(config)
-    model.load_state_dict(state_dict)
-    model.eval()
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-
-    # load image processor
-    processor = DetaImageProcessor(format="coco_detection")
-
-    # verify our conversion on image
-    img = prepare_img()
-    encoding = processor(images=img, return_tensors="pt")
-    pixel_values = encoding["pixel_values"]
-    outputs = model(pixel_values.to(device))
-
-    # verify logits
-    print("Logits:", outputs.logits[0, :3, :3])
-    print("Boxes:", outputs.pred_boxes[0, :3, :3])
-    if model_name == "deta-swin-large":
-        expected_logits = torch.tensor(
-            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]
-        )
-        expected_boxes = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]])
-    elif model_name == "deta-swin-large-o365":
-        expected_logits = torch.tensor(
-            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]
-        )
-        expected_boxes = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]])
-    assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
-    assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
-    print("Everything ok!")
-
-    if pytorch_dump_folder_path:
-        # Save model and processor
-        logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}...")
-        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
-        model.save_pretrained(pytorch_dump_folder_path)
-        processor.save_pretrained(pytorch_dump_folder_path)
-
-    # Push to hub
-    if push_to_hub:
-        print("Pushing model and processor to hub...")
-        model.push_to_hub(f"jozhang97/{model_name}")
-        processor.push_to_hub(f"jozhang97/{model_name}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_name",
-        type=str,
-        default="deta-swin-large",
-        choices=["deta-swin-large", "deta-swin-large-o365"],
-        help="Name of the model you'd like to convert.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        help="Path to the folder to output PyTorch model.",
-    )
-    parser.add_argument(
-        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
-    )
-    args = parser.parse_args()
-    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

src/transformers/models/deprecated/efficientformer/convert_efficientformer_original_pytorch_checkpoint_to_pytorch.py

@@ -1,252 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Convert EfficientFormer checkpoints from the original repository.
-
-URL: https://github.com/snap-research/EfficientFormer
-"""
-
-import argparse
-import re
-from pathlib import Path
-
-import requests
-import torch
-from PIL import Image
-from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
-
-from transformers import (
-    EfficientFormerConfig,
-    EfficientFormerForImageClassificationWithTeacher,
-    EfficientFormerImageProcessor,
-)
-from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
-
-
-def rename_key(old_name, num_meta4D_last_stage):
-    new_name = old_name
-
-    if "patch_embed" in old_name:
-        _, layer, param = old_name.split(".")
-
-        if layer == "0":
-            new_name = old_name.replace("0", "convolution1")
-        elif layer == "1":
-            new_name = old_name.replace("1", "batchnorm_before")
-        elif layer == "3":
-            new_name = old_name.replace("3", "convolution2")
-        else:
-            new_name = old_name.replace("4", "batchnorm_after")
-
-    if "network" in old_name and re.search(r"\d\.\d", old_name):
-        two_digit_num = r"\b\d{2}\b"
-        if bool(re.search(two_digit_num, old_name)):
-            match = re.search(r"\d\.\d\d.", old_name).group()
-        else:
-            match = re.search(r"\d\.\d.", old_name).group()
-        if int(match[0]) < 6:
-            trimmed_name = old_name.replace(match, "")
-            trimmed_name = trimmed_name.replace("network", match[0] + ".meta4D_layers.blocks." + match[2:-1])
-            new_name = "intermediate_stages." + trimmed_name
-        else:
-            trimmed_name = old_name.replace(match, "")
-            if int(match[2]) < num_meta4D_last_stage:
-                trimmed_name = trimmed_name.replace("network", "meta4D_layers.blocks." + match[2])
-            else:
-                layer_index = str(int(match[2]) - num_meta4D_last_stage)
-                trimmed_name = trimmed_name.replace("network", "meta3D_layers.blocks." + layer_index)
-                if "norm1" in old_name:
-                    trimmed_name = trimmed_name.replace("norm1", "layernorm1")
-                elif "norm2" in old_name:
-                    trimmed_name = trimmed_name.replace("norm2", "layernorm2")
-                elif "fc1" in old_name:
-                    trimmed_name = trimmed_name.replace("fc1", "linear_in")
-                elif "fc2" in old_name:
-                    trimmed_name = trimmed_name.replace("fc2", "linear_out")
-
-            new_name = "last_stage." + trimmed_name
-
-    elif "network" in old_name and re.search(r".\d.", old_name):
-        new_name = old_name.replace("network", "intermediate_stages")
-
-    if "fc" in new_name:
-        new_name = new_name.replace("fc", "convolution")
-    elif ("norm1" in new_name) and ("layernorm1" not in new_name):
-        new_name = new_name.replace("norm1", "batchnorm_before")
-    elif ("norm2" in new_name) and ("layernorm2" not in new_name):
-        new_name = new_name.replace("norm2", "batchnorm_after")
-    if "proj" in new_name:
-        new_name = new_name.replace("proj", "projection")
-    if "dist_head" in new_name:
-        new_name = new_name.replace("dist_head", "distillation_classifier")
-    elif "head" in new_name:
-        new_name = new_name.replace("head", "classifier")
-    elif "patch_embed" in new_name:
-        new_name = "efficientformer." + new_name
-    elif new_name == "norm.weight" or new_name == "norm.bias":
-        new_name = new_name.replace("norm", "layernorm")
-        new_name = "efficientformer." + new_name
-    else:
-        new_name = "efficientformer.encoder." + new_name
-
-    return new_name
-
-
-def convert_torch_checkpoint(checkpoint, num_meta4D_last_stage):
-    for key in checkpoint.copy().keys():
-        val = checkpoint.pop(key)
-        checkpoint[rename_key(key, num_meta4D_last_stage)] = val
-
-    return checkpoint
-
-
-# We will verify our results on a COCO image
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    image = Image.open(requests.get(url, stream=True).raw)
-
-    return image
-
-
-def convert_efficientformer_checkpoint(
-    checkpoint_path: Path, efficientformer_config_file: Path, pytorch_dump_path: Path, push_to_hub: bool
-):
-    orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-    config = EfficientFormerConfig.from_json_file(efficientformer_config_file)
-    model = EfficientFormerForImageClassificationWithTeacher(config)
-    model_name = "_".join(checkpoint_path.split("/")[-1].split(".")[0].split("_")[:-1])
-
-    num_meta4D_last_stage = config.depths[-1] - config.num_meta3d_blocks + 1
-    new_state_dict = convert_torch_checkpoint(orig_state_dict, num_meta4D_last_stage)
-
-    model.load_state_dict(new_state_dict)
-    model.eval()
-
-    pillow_resamplings = {
-        "bilinear": PILImageResampling.BILINEAR,
-        "bicubic": PILImageResampling.BICUBIC,
-        "nearest": PILImageResampling.NEAREST,
-    }
-
-    # prepare image
-    image = prepare_img()
-    image_size = 256
-    crop_size = 224
-    processor = EfficientFormerImageProcessor(
-        size={"shortest_edge": image_size},
-        crop_size={"height": crop_size, "width": crop_size},
-        resample=pillow_resamplings["bicubic"],
-    )
-    pixel_values = processor(images=image, return_tensors="pt").pixel_values
-
-    # original processing pipeline
-    image_transforms = Compose(
-        [
-            Resize(image_size, interpolation=pillow_resamplings["bicubic"]),
-            CenterCrop(crop_size),
-            ToTensor(),
-            Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
-        ]
-    )
-    original_pixel_values = image_transforms(image).unsqueeze(0)
-
-    assert torch.allclose(original_pixel_values, pixel_values)
-
-    outputs = model(pixel_values)
-    logits = outputs.logits
-
-    expected_shape = (1, 1000)
-
-    if "l1" in model_name:
-        expected_logits = torch.Tensor(
-            [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]
-        )
-        assert torch.allclose(logits[0, :10], expected_logits, atol=1e-3)
-        assert logits.shape == expected_shape
-    elif "l3" in model_name:
-        expected_logits = torch.Tensor(
-            [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]
-        )
-        assert torch.allclose(logits[0, :10], expected_logits, atol=1e-3)
-        assert logits.shape == expected_shape
-    elif "l7" in model_name:
-        expected_logits = torch.Tensor(
-            [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]
-        )
-        assert logits.shape == expected_shape
-    else:
-        raise ValueError(
-            f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"
-        )
-
-    # Save Checkpoints
-    Path(pytorch_dump_path).mkdir(exist_ok=True)
-    model.save_pretrained(pytorch_dump_path)
-    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")
-    processor.save_pretrained(pytorch_dump_path)
-    print(f"Processor successfuly saved at {pytorch_dump_path}")
-
-    if push_to_hub:
-        print("Pushing model to the hub...")
-
-        model.push_to_hub(
-            repo_id=f"Bearnardd/{pytorch_dump_path}",
-            commit_message="Add model",
-            use_temp_dir=True,
-        )
-        processor.push_to_hub(
-            repo_id=f"Bearnardd/{pytorch_dump_path}",
-            commit_message="Add image processor",
-            use_temp_dir=True,
-        )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--pytorch_model_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to EfficientFormer pytorch checkpoint.",
-    )
-    parser.add_argument(
-        "--config_file",
-        default=None,
-        type=str,
-        required=True,
-        help="The json file for EfficientFormer model config.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
-    )
-
-    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
-    parser.add_argument(
-        "--no-push_to_hub",
-        dest="push_to_hub",
-        action="store_false",
-        help="Do not push model and image processor to the hub",
-    )
-    parser.set_defaults(push_to_hub=True)
-
-    args = parser.parse_args()
-    convert_efficientformer_checkpoint(
-        checkpoint_path=args.pytorch_model_path,
-        efficientformer_config_file=args.config_file,
-        pytorch_dump_path=args.pytorch_dump_path,
-        push_to_hub=args.push_to_hub,
-    )

src/transformers/models/deprecated/gptsan_japanese/convert_gptsan_tf_checkpoint_to_pytorch.py

@@ -1,181 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Convert GPTSANJapanese checkpoints from the original repository to pytorch model."""
-
-import argparse
-import json
-import os
-from collections import OrderedDict
-
-import numpy as np
-import tensorflow as tf
-import torch
-
-
-def convert_tf_gptsan_to_pt(args):
-    parameter_file = os.path.join(args.tf_model_dir, "parameters.json")
-    params = json.loads(open(parameter_file).read())
-    if not params:
-        raise ValueError(
-            f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."
-        )
-    if not args.output.endswith(".pt"):
-        args.output = args.output + ".pt"
-    new_state = OrderedDict()
-    with tf.device("/CPU:0"):
-        reader = tf.train.load_checkpoint(args.tf_model_dir)
-        shapes = reader.get_variable_to_shape_map()
-        for key_name in shapes.keys():
-            vnp = reader.get_tensor(key_name).astype(np.float16)
-            if key_name.endswith("/adam_m") or key_name.endswith("/adam_v"):
-                continue
-            if key_name.startswith("pasts/"):
-                if key_name.startswith("pasts/mlp"):
-                    player = int(key_name[9])
-                elif key_name.startswith("pasts/out"):
-                    player = 8
-                name = "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
-                state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/moe"):
-                player = int(key_name[9:].split("/")[0])
-                if key_name.endswith("/switch_gating/kernel"):
-                    name = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
-                    state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/softmlp/kernel"):
-                    name = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
-                    state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/wo/kernel") or key_name.endswith("/wi/kernel"):
-                    nlayer = key_name[-9:-7]
-                    for i in range(16):
-                        name = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
-                        state = (
-                            vnp[i].transpose([1, 0]).copy()
-                        )  # In Mesh-Tensorflow, it is one array, so it is divided
-                        new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/mlp"):
-                player = int(key_name[9:].split("/")[0])
-                if key_name.endswith("/p1/kernel"):
-                    name = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
-                    state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/p1/bias"):
-                    name = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/p2/kernel"):
-                    name = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
-                    state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/p2/bias"):
-                    name = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/ln"):
-                player = int(key_name[8:].split("/")[0])
-                if key_name.endswith("/b"):
-                    name = "model.blocks.%d.feed_forward.norm.bias" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/g"):
-                    name = "model.blocks.%d.feed_forward.norm.weight" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/att"):
-                player = int(key_name[9:].split("/")[0])
-                if key_name.endswith("/qkv/kernel"):
-                    state = vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
-                    state_q = state[:, 0, :, :]
-                    state_k = state[:, 1, :, :]
-                    state_v = state[:, 2, :, :]
-                    state_q = (
-                        state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]])
-                        .transpose([1, 0])
-                        .copy()
-                    )  # Mesh-Tensorflow is a diagonal matrix
-                    state_k = (
-                        state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]])
-                        .transpose([1, 0])
-                        .copy()
-                    )  # Mesh-Tensorflow is a diagonal matrix
-                    state_v = (
-                        state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]])
-                        .transpose([1, 0])
-                        .copy()
-                    )  # Mesh-Tensorflow is a diagonal matrix
-                    name = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
-                    new_state[name] = torch.tensor(state_q)
-                    name = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
-                    new_state[name] = torch.tensor(state_k)
-                    name = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
-                    new_state[name] = torch.tensor(state_v)
-                elif key_name.endswith("/o/kernel"):
-                    name = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
-                    state = (
-                        vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]).transpose([1, 0]).copy()
-                    )  # Mesh-Tensorflow is a diagonal matrix
-                    new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/an"):
-                player = int(key_name[8:].split("/")[0])
-                if key_name.endswith("/b"):
-                    name = "model.blocks.%d.self_attn.norm.bias" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-                elif key_name.endswith("/g"):
-                    name = "model.blocks.%d.self_attn.norm.weight" % player
-                    state = vnp.copy()  # same because it is one dimensional
-                    new_state[name] = torch.tensor(state)
-            elif (
-                key_name.startswith("model/wte")
-                or key_name.startswith("model/wpe")
-                or key_name.startswith("model/ete")
-            ):
-                nlayer = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
-                    key_name[-3:]
-                ]
-                name = "model.%s.weight" % nlayer
-                state = vnp.copy()  # same in embedded
-                new_state[name] = torch.tensor(state)
-                if key_name.startswith("model/wte"):
-                    name = "lm_head.weight"
-                    state = vnp.copy()  # same in embedded
-                    new_state[name] = torch.tensor(state)
-            elif key_name.startswith("model/wob"):
-                name = "final_logits_bias"
-                state = vnp.copy()  # same in embedded
-                state = state.reshape((1, -1))
-                new_state[name] = torch.tensor(state)
-            elif key_name == "model/dense/kernel":
-                name = "model.last_project.weight"
-                state = vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
-                new_state[name] = torch.tensor(state)
-            elif key_name == "model/dense_1/bias":
-                name = "model.last_project.bias"
-                state = vnp.copy()  # same because it is one dimensional
-                new_state[name] = torch.tensor(state)
-    torch.save(new_state, args.output)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
-    parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
-    args = parser.parse_args()
-    convert_tf_gptsan_to_pt(args)