Drop unused imports from test (#49973)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49973

From
```
./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/
```

Test Plan: Standard sandcastle tests

Reviewed By: xush6528

Differential Revision: D25727350

fbshipit-source-id: 237ec4edd85788de920663719173ebec7ddbae1c

test/jit/test_async.py

@@ -11,7 +11,7 @@ from typing import Any
 pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
 sys.path.append(pytorch_test_dir)
 from torch.testing._internal.jit_utils import JitTestCase, _inline_everything
-from typing import List, Tuple
+from typing import List
 from torch import Tensor
 
 class TestAsync(JitTestCase):

test/jit/test_list_dict.py

@@ -1,13 +1,12 @@
 import os
 import sys
 import inspect
-from typing import Dict, List, Optional, Tuple, Any
+from typing import Dict, List, Any
 from textwrap import dedent
 from collections import OrderedDict
 
 import torch
 from torch.testing import FileCheck
-from torch import Tensor
 
 # Make the helper files in test/ importable
 pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

test/jit/test_recursive_script.py

@@ -2,11 +2,10 @@ import os
 import sys
 import typing
 import typing_extensions
-from typing import List, Dict, Optional, Tuple
+from typing import List, Dict, Optional
 
 import torch
 import torch.nn as nn
-from torch import Tensor
 from torch.testing import FileCheck
 from collections import OrderedDict
 

test/jit/test_save_load.py

@@ -5,7 +5,6 @@ import sys
 import random
 import torch
 from itertools import product as product
-from torch import Tensor
 from torch.testing._internal.common_utils import TemporaryFileName
 from typing import NamedTuple, Optional
 

test/quantization/test_quantize.py

@@ -82,6 +82,7 @@ import torch.testing._internal.hypothesis_utils as hu
 hu.assert_deadline_disabled()
 
 # Standard library
+from typing import Tuple
 import copy
 import io
 import unittest
@@ -1007,10 +1008,7 @@ class TestPostTrainingDynamic(QuantizationTestCase):
                     super(ScriptWrapperPackedLSTM, self).__init__()
                     self.cell = cell
 
-                def forward(self,
-                            x  # type: PackedSequence
-                            ):
-                    # type: (...) -> Tuple[PackedSequence, Tuple[torch.Tensor, torch.Tensor]]
+                def forward(self, x: PackedSequence) -> Tuple[PackedSequence, Tuple[torch.Tensor, torch.Tensor]]:
                     return self.cell(x)
 
             class ScriptWrapperPackedGRU(torch.nn.Module):
@@ -1018,10 +1016,7 @@ class TestPostTrainingDynamic(QuantizationTestCase):
                     super(ScriptWrapperPackedGRU, self).__init__()
                     self.cell = cell
 
-                def forward(self,
-                            x  # type: PackedSequence
-                            ):
-                    # type: (...) -> Tuple[PackedSequence, torch.Tensor]
+                def forward(self, x: PackedSequence) -> Tuple[PackedSequence, torch.Tensor]:
                     return self.cell(x)
 
             script_wrapper_map = {'LSTM': ScriptWrapperPackedLSTM,

test/test_jit.py

@@ -44,6 +44,7 @@ from torch._C import TensorType, BoolType, parse_ir, _propagate_shapes
 from torch._six import PY37, StringIO
 from torch.autograd import Variable
 from torch.jit.annotations import BroadcastingList2, BroadcastingList3, Any  # noqa: F401
+from torch.nn.utils.rnn import PackedSequence
 from torch.testing import FileCheck
 import torch.autograd.profiler
 import torch.cuda
@@ -80,30 +81,30 @@ from torch.testing._internal.test_module.no_future_div import div_int_nofuture,
 
 # Standard library
 from collections import defaultdict, namedtuple, OrderedDict
-import copy
 from copy import deepcopy
 from itertools import product
-import itertools
 from textwrap import dedent
 from typing import List, Dict, NamedTuple, Optional, Tuple, Union
-import inspect
-import math
+import copy
 import functools
-import numpy as np
+import inspect
 import io
+import itertools
+import math
+import numpy as np
 import os
 import pickle
 import pickletools
 import random
+import re
 import shutil
+import string
 import sys
 import tempfile
 import types
 import unittest
 import warnings
 import zipfile
-import re
-import string
 
 
 def canonical(graph):
@@ -14369,7 +14370,6 @@ dedent """
         self.assertEqual(eager_out, script_out)
 
     def test_nn_LSTM(self):
-        from torch.nn.utils.rnn import PackedSequence
         input = torch.nn.utils.rnn.pack_sequence([torch.randn(5, 5)])
 
         class S(torch.jit.ScriptModule):
@@ -14378,8 +14378,7 @@ dedent """
                 self.x = torch.nn.LSTM(5, 5)
 
             @torch.jit.script_method
-            def forward(self, input):
-                # type: (PackedSequence) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]  # noqa
+            def forward(self, input: PackedSequence) -> Tuple[PackedSequence, Tuple[torch.Tensor, torch.Tensor]]:
                 return self.x(input)
 
         eager_out = self.runAndSaveRNG(lambda x: torch.nn.LSTM(5, 5)(x), (input,))[0]
@@ -14388,7 +14387,6 @@ dedent """
         self.assertEqual(eager_out, script_out)
 
     def test_nn_GRU(self):
-        from torch.nn.utils.rnn import PackedSequence
         seq_input = torch.nn.utils.rnn.pack_sequence([torch.randn(5, 5)])
         tensor_input = torch.randn(5, 5, 5)
 
@@ -14398,8 +14396,7 @@ dedent """
                 self.x = torch.nn.GRU(5, 5)
 
             @torch.jit.script_method
-            def forward(self, input):
-                # type: (PackedSequence) -> Tuple[PackedSequence, Tensor]
+            def forward(self, input: PackedSequence) -> Tuple[PackedSequence, torch.Tensor]:
                 return self.x(input)
 
         class TensorGRU(torch.jit.ScriptModule):
@@ -14408,8 +14405,7 @@ dedent """
                 self.x = torch.nn.GRU(5, 5)
 
             @torch.jit.script_method
-            def forward(self, input):
-                # type: (Tensor) -> Tuple[Tensor, Tensor]
+            def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
                 return self.x(input)
 
         seq_eager_out = self.runAndSaveRNG(lambda x: torch.nn.GRU(5, 5)(x), (seq_input,))[0]

test/test_linalg.py

@@ -3845,7 +3845,6 @@ class TestLinalg(TestCase):
         """Compare torch and scipy.sparse.linalg implementations of lobpcg
         """
         import time
-        import scipy
         from torch.testing._internal.common_utils import random_sparse_pd_matrix
         from torch._linalg_utils import matmul as mm
         from scipy.sparse.linalg import lobpcg as scipy_lobpcg

test/test_numba_integration.py

@@ -1,7 +1,7 @@
 import unittest
 
 import torch.testing._internal.common_utils as common
-from torch.testing._internal.common_utils import TEST_NUMBA, TEST_NUMPY
+from torch.testing._internal.common_utils import TEST_NUMPY
 from torch.testing._internal.common_cuda import TEST_NUMBA_CUDA, TEST_CUDA, TEST_MULTIGPU
 
 import torch
@@ -9,9 +9,6 @@ import torch
 if TEST_NUMPY:
     import numpy
 
-if TEST_NUMBA:
-    import numba
-
 if TEST_NUMBA_CUDA:
     import numba.cuda