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https://github.com/pytorch/pytorch.git
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46484e8dfe
Summary: This PR simplifies `.github/scripts/generate_ci_workflows.py` by using the same strategy as https://github.com/pytorch/pytorch/issues/54344, representing workflows as plain data to avoid duplicating the definition of the `generate_workflow_file` function. This will make the script easier to maintain if/when that function is modified and/or more workflow types are added. Pull Request resolved: https://github.com/pytorch/pytorch/pull/58491 Test Plan: The Lint job in CI; specifically: ``` make generate-gha-workflows mypy --config mypy-strict.ini ``` Reviewed By: malfet, seemethere Differential Revision: D28511918 Pulled By: samestep fbshipit-source-id: aaf415a954d938a29aee7c9367c9bc2b9f44bb01
187 lines
7.4 KiB
Python
Executable File
187 lines
7.4 KiB
Python
Executable File
#!/usr/bin/env python3
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from pathlib import Path
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from typing import Any, Dict
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import jinja2
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DOCKER_REGISTRY = "308535385114.dkr.ecr.us-east-1.amazonaws.com"
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GITHUB_DIR = Path(__file__).parent.parent
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# it would be nice to statically specify that build_environment must be
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# present, but currently Python has no easy way to do that
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# https://github.com/python/mypy/issues/4617
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PyTorchWorkflow = Dict[str, Any]
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def PyTorchWindowsWorkflow(
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*,
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build_environment: str,
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on_pull_request: bool = False
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) -> PyTorchWorkflow:
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CPU_TEST_RUNNER = "windows.4xlarge"
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CUDA_TEST_RUNNER = "windows.8xlarge.nvidia.gpu"
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return {
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"build_environment": build_environment,
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"test_runner_type": (
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CUDA_TEST_RUNNER
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if "cuda" in build_environment
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else CPU_TEST_RUNNER
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),
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"on_pull_request": on_pull_request,
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}
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def PyTorchLinuxWorkflow(
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*,
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build_environment: str,
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docker_image_base: str,
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on_pull_request: bool = False,
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enable_doc_jobs: bool = False,
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) -> PyTorchWorkflow:
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CPU_TEST_RUNNER = "linux.2xlarge"
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CUDA_TEST_RUNNER = "linux.8xlarge.nvidia.gpu"
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return {
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"build_environment": build_environment,
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"docker_image_base": docker_image_base,
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"test_runner_type": (
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CUDA_TEST_RUNNER
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if "cuda" in build_environment
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else CPU_TEST_RUNNER
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),
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"on_pull_request": on_pull_request,
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"enable_doc_jobs": enable_doc_jobs,
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}
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def generate_workflow_file(
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*,
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workflow: PyTorchWorkflow,
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workflow_template: jinja2.Template,
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) -> Path:
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output_file_path = GITHUB_DIR / f"workflows/{workflow['build_environment']}.yml"
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with open(output_file_path, "w") as output_file:
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output_file.writelines(["# @generated DO NOT EDIT MANUALLY\n"])
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output_file.write(workflow_template.render(**workflow))
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output_file.write("\n")
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return output_file_path
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WINDOWS_WORKFLOWS = [
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PyTorchWindowsWorkflow(
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build_environment="pytorch-win-vs2019-cpu-py3",
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)
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]
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LINUX_WORKFLOWS = [
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PyTorchLinuxWorkflow(
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build_environment="pytorch-linux-xenial-py3.6-gcc5.4",
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docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3.6-gcc5.4",
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on_pull_request=True,
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enable_doc_jobs=True,
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),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-paralleltbb-linux-xenial-py3.6-gcc5.4",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3.6-gcc5.4",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-parallelnative-linux-xenial-py3.6-gcc5.4",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3.6-gcc5.4",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-pure_torch-linux-xenial-py3.6-gcc5.4",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3.6-gcc5.4",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-gcc7",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3.6-gcc7",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-asan",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-asan",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang7-onnx",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang7-onnx",
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# ),
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PyTorchLinuxWorkflow(
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build_environment="pytorch-linux-xenial-cuda10.2-cudnn7-py3.6-gcc7",
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docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7",
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),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-cuda11.1-cudnn8-py3.6-gcc7",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-libtorch-linux-xenial-cuda11.1-cudnn8-py3.6-gcc7",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-bionic-py3.6-clang9-noarch",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-bionic-py3.6-clang9",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-xla-linux-bionic-py3.6-clang9",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-bionic-py3.6-clang9",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-vulkan-linux-bionic-py3.6-clang9",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-bionic-py3.6-clang9",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-bionic-py3.8-gcc9-coverage",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-bionic-py3.8-gcc9",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-bionic-rocm3.9-py3.6",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-bionic-rocm3.9-py3.6",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-android-ndk-r19c-x86_32",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-android-ndk-r19c-x86_64",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-android-ndk-r19c-arm-v7a",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-android-ndk-r19c-arm-v8a",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-mobile",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-asan",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-mobile-custom-dynamic",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-mobile-custom-static",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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# PyTorchLinuxWorkflow(
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# build_environment="pytorch-linux-xenial-py3.6-clang5-mobile-code-analysis",
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# docker_image_base=f"{DOCKER_REGISTRY}/pytorch/pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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# ),
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]
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if __name__ == "__main__":
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jinja_env = jinja2.Environment(
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variable_start_string="!{{",
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loader=jinja2.FileSystemLoader(str(GITHUB_DIR.joinpath("templates"))),
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)
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template_and_workflows = [
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(jinja_env.get_template("linux_ci_workflow.yml.in"), LINUX_WORKFLOWS),
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(jinja_env.get_template("windows_ci_workflow.yml.in"), WINDOWS_WORKFLOWS)
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]
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for template, workflows in template_and_workflows:
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for workflow in workflows:
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print(generate_workflow_file(workflow=workflow, workflow_template=template))
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