



# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  a__												= data



 def __iter__(	self      ) ->			Any:
  for element in self.data:
   yield element






def     __a					(		__UpperCAmelCase=True       ):
 a__												= Accelerator(even_batches=__UpperCAmelCase       )
 assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
 return accelerator






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase = False       ):
 if iterable:
  a__												= DummyIterableDataset(torch.as_tensor(range(__UpperCAmelCase       )       )       )
 else:
  a__												= TensorDataset(torch.as_tensor(range(__UpperCAmelCase       )       )       )

 a__												= DataLoader(__UpperCAmelCase							,  batch_size=__UpperCAmelCase       )
 a__												= accelerator.prepare(__UpperCAmelCase       )

 return dl






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  ):
 a__												= create_dataloader(accelerator=__UpperCAmelCase							,  dataset_size=__UpperCAmelCase							,  batch_size=__UpperCAmelCase       )

 a__												= [len(batch[0]       ) for batch in dl]

 if accelerator.process_index == 0:
  assert batch_sizes == process_0_expected_batch_sizes
 elif accelerator.process_index == 1:
  assert batch_sizes == process_1_expected_batch_sizes






def     __a					(		):
 a__												= create_accelerator()

 # without padding, we would expect a different number of batches
 verify_dataloader_batch_sizes(
     __UpperCAmelCase							,  dataset_size=3							,  batch_size=1							,  process_0_expected_batch_sizes=[1, 1]							,  process_1_expected_batch_sizes=[1, 1]							,  )

 # without padding, we would expect the same number of batches, but different sizes
 verify_dataloader_batch_sizes(
     __UpperCAmelCase							,  dataset_size=7							,  batch_size=2							,  process_0_expected_batch_sizes=[2, 2]							,  process_1_expected_batch_sizes=[2, 2]							,  )






def     __a					(		):
 a__												= create_accelerator(even_batches=__UpperCAmelCase       )

 verify_dataloader_batch_sizes(
     __UpperCAmelCase							,  dataset_size=3							,  batch_size=1							,  process_0_expected_batch_sizes=[1, 1]							,  process_1_expected_batch_sizes=[1]							,  )

 verify_dataloader_batch_sizes(
     __UpperCAmelCase							,  dataset_size=7							,  batch_size=2							,  process_0_expected_batch_sizes=[2, 2]							,  process_1_expected_batch_sizes=[2, 1]							,  )






def     __a					(		):
 a__												= create_accelerator(even_batches=__UpperCAmelCase       )

 a__												= torch.nn.Linear(1							,  1       )
 a__												= accelerator.prepare(__UpperCAmelCase       )

 a__												= create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1       )

 a__												= []
 with accelerator.join_uneven_inputs([ddp_model]       ):
  for batch_idx, batch in enumerate(__UpperCAmelCase       ):
   a__												= ddp_model(batch[0].float()       )
   a__												= output.sum()
   loss.backward()
   batch_idxs.append(__UpperCAmelCase       )

 accelerator.wait_for_everyone()

 if accelerator.process_index == 0:
  assert batch_idxs == [0, 1]
 elif accelerator.process_index == 1:
  assert batch_idxs == [0]






def     __a					(		__UpperCAmelCase       ):
 with warnings.catch_warnings(record=__UpperCAmelCase       ) as w:
  with accelerator.join_uneven_inputs([Mock()]       ):
   pass

  assert issubclass(w[-1].category							,  __UpperCAmelCase       )
  assert "only supported for multi-GPU" in str(w[-1].message       )






def     __a					(		):
 a__												= True
 a__												= False
 a__												= create_accelerator(even_batches=__UpperCAmelCase       )
 a__												= torch.nn.Linear(1							,  1       )
 a__												= accelerator.prepare(__UpperCAmelCase       )
 a__												= create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1       )
 a__												= create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1       )

 with accelerator.join_uneven_inputs([ddp_model]							,  even_batches=__UpperCAmelCase       ):
  a__												= train_dl.batch_sampler.even_batches
  a__												= valid_dl.batch_sampler.even_batches

 assert train_dl_overridden_value == overridden_even_batches
 assert valid_dl_overridden_value == overridden_even_batches
 assert train_dl.batch_sampler.even_batches == default_even_batches
 assert valid_dl.batch_sampler.even_batches == default_even_batches






def     __a					(		):
 a__												= True
 a__												= False
 a__												= create_accelerator(even_batches=__UpperCAmelCase       )
 a__												= torch.nn.Linear(1							,  1       )
 a__												= accelerator.prepare(__UpperCAmelCase       )
 create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1							,  iterable=__UpperCAmelCase       )
 a__												= create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1       )

 with warnings.catch_warnings():
  warnings.filterwarnings('''ignore'''       )
  try:
   with accelerator.join_uneven_inputs([ddp_model]							,  even_batches=__UpperCAmelCase       ):
    a__												= batch_dl.batch_sampler.even_batches
  except AttributeError:
   # ensure attribute error is not raised when processing iterable dl
   raise AssertionError

 assert batch_dl_overridden_value == overridden_even_batches
 assert batch_dl.batch_sampler.even_batches == default_even_batches






def     __a					(		):
 a__												= create_accelerator()
 a__												= torch.nn.Linear(1							,  1       )
 a__												= accelerator.prepare(__UpperCAmelCase       )
 create_dataloader(__UpperCAmelCase							,  dataset_size=3							,  batch_size=1							,  iterable=__UpperCAmelCase       )

 with warnings.catch_warnings(record=__UpperCAmelCase       ) as w:
  with accelerator.join_uneven_inputs([ddp_model]							,  even_batches=__UpperCAmelCase       ):
   pass

  assert issubclass(w[-1].category							,  __UpperCAmelCase       )
  assert "only supported for map-style datasets" in str(w[-1].message       )






def     __a					(		):
 a__												= create_accelerator()

 accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''       )
 test_default_ensures_even_batch_sizes()

 accelerator.print('''Run tests with even_batches disabled'''       )
 test_can_disable_even_batches()

 accelerator.print('''Test joining uneven inputs'''       )
 test_can_join_uneven_inputs()

 accelerator.print('''Test overriding even_batches when joining uneven inputs'''       )
 test_join_can_override_even_batches()

 accelerator.print('''Test overriding even_batches for mixed dataloader types'''       )
 test_join_can_override_for_mixed_type_dataloaders()

 accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''       )
 test_join_raises_warning_for_iterable_when_overriding_even_batches()

 accelerator.print('''Test join with non DDP distributed raises warning'''       )
 a__												= accelerator.state.distributed_type
 a__												= DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(__UpperCAmelCase       )
 a__												= original_state


if __name__ == "__main__":
     main()










import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_      :      Optional[int]    =     '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a_      :      List[str]    =     [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

 with open(os.path.join(__UpperCAmelCase							,  '''utils'''							,  '''tf_ops'''							,  '''onnx.json'''       )       ) as f:
  a__												= json.load(__UpperCAmelCase       )['''opsets''']

 for i in range(1							,  opset + 1       ):
  onnx_ops.extend(onnx_opsets[str(__UpperCAmelCase       )]       )

 with open(__UpperCAmelCase							,  '''rb'''       ) as f:
  saved_model.ParseFromString(f.read()       )

 a__												= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node       )

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
 a__												= sorted(__UpperCAmelCase       )
 a__												= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(__UpperCAmelCase       )

 if strict and len(__UpperCAmelCase       ) > 0:
  raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops       )
 elif len(__UpperCAmelCase       ) > 0:
  print(f"Found the following incompatible ops for the opset {opset}:"       )
  print(*__UpperCAmelCase							,  sep='''\n'''       )
 else:
  print(f"The saved model {saved_model_path} can properly be converted with ONNX."       )


if __name__ == "__main__":
     a_      :      Dict    =     argparse.ArgumentParser()
     parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
     parser.add_argument(
         '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
     )
     parser.add_argument(
         '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
     )
     parser.add_argument(
         '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
     )
     a_      :      str    =     parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)





