






# 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 			__a  (    A__       ):







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  data







 def __iter__(		self					:   Union[str, Any]						):


      '''simple docstring'''






      for element in self.data:
           yield element







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase=True			) ->	Optional[int]:
     UpperCamelCase__			:   Dict       				=  Accelerator(even_batches=__lowerCAmelCase			)
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = False			) ->	Union[str, Any]:
     if iterable:
          UpperCamelCase__			:   Union[str, Any]       				=  DummyIterableDataset(torch.as_tensor(range(__lowerCAmelCase			)			)			)
     else:
          UpperCamelCase__			:   Union[str, Any]       				=  TensorDataset(torch.as_tensor(range(__lowerCAmelCase			)			)			)

     UpperCamelCase__			:   Any       				=  DataLoader(__lowerCAmelCase					,    batch_size=__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[int]       				=  accelerator.prepare(__lowerCAmelCase			)

     return dl







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    ) ->	str:
     UpperCamelCase__			:   str       				=  create_dataloader(accelerator=__lowerCAmelCase					,    dataset_size=__lowerCAmelCase					,    batch_size=__lowerCAmelCase			)

     UpperCamelCase__			:   Optional[Any]       				=  [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 		SCREAMING_SNAKE_CASE							(					) ->	Union[str, Any]:
     UpperCamelCase__			:   Union[str, Any]       				=  create_accelerator()

     # without padding, we would expect a different number of batches
     verify_dataloader_batch_sizes(
         __lowerCAmelCase					,    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(
         __lowerCAmelCase					,    dataset_size=7					,    batch_size=2					,    process_0_expected_batch_sizes=[2, 2]					,    process_1_expected_batch_sizes=[2, 2]					,    )







def 		SCREAMING_SNAKE_CASE							(					) ->	Any:
     UpperCamelCase__			:   str       				=  create_accelerator(even_batches=__lowerCAmelCase			)

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

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







def 		SCREAMING_SNAKE_CASE							(					) ->	List[Any]:
     UpperCamelCase__			:   Optional[int]       				=  create_accelerator(even_batches=__lowerCAmelCase			)

     UpperCamelCase__			:   int       				=  torch.nn.Linear(1					,    1			)
     UpperCamelCase__			:   int       				=  accelerator.prepare(__lowerCAmelCase			)

     UpperCamelCase__			:   List[str]       				=  create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1			)

     UpperCamelCase__			:   List[str]       				=  []
     with accelerator.join_uneven_inputs([ddp_model]			):
          for batch_idx, batch in enumerate(__lowerCAmelCase			):
               UpperCamelCase__			:   Dict       				=  ddp_model(batch[0].float()			)
               UpperCamelCase__			:   Union[str, Any]       				=  output.sum()
               loss.backward()
               batch_idxs.append(__lowerCAmelCase			)

     accelerator.wait_for_everyone()

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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     with warnings.catch_warnings(record=__lowerCAmelCase			) as w:
          with accelerator.join_uneven_inputs([Mock()]			):
               pass

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







def 		SCREAMING_SNAKE_CASE							(					) ->	int:
     UpperCamelCase__			:   Union[str, Any]       				=  True
     UpperCamelCase__			:   List[Any]       				=  False
     UpperCamelCase__			:   Dict       				=  create_accelerator(even_batches=__lowerCAmelCase			)
     UpperCamelCase__			:   Union[str, Any]       				=  torch.nn.Linear(1					,    1			)
     UpperCamelCase__			:   List[str]       				=  accelerator.prepare(__lowerCAmelCase			)
     UpperCamelCase__			:   Any       				=  create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1			)
     UpperCamelCase__			:   Tuple       				=  create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1			)

     with accelerator.join_uneven_inputs([ddp_model]					,    even_batches=__lowerCAmelCase			):
          UpperCamelCase__			:   int       				=  train_dl.batch_sampler.even_batches
          UpperCamelCase__			:   Tuple       				=  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 		SCREAMING_SNAKE_CASE							(					) ->	List[str]:
     UpperCamelCase__			:   Tuple       				=  True
     UpperCamelCase__			:   Tuple       				=  False
     UpperCamelCase__			:   List[str]       				=  create_accelerator(even_batches=__lowerCAmelCase			)
     UpperCamelCase__			:   str       				=  torch.nn.Linear(1					,    1			)
     UpperCamelCase__			:   Any       				=  accelerator.prepare(__lowerCAmelCase			)
     create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1					,    iterable=__lowerCAmelCase			)
     UpperCamelCase__			:   Any       				=  create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1			)

     with warnings.catch_warnings():
          warnings.filterwarnings("ignore"			)
          try:
               with accelerator.join_uneven_inputs([ddp_model]					,    even_batches=__lowerCAmelCase			):
                    UpperCamelCase__			:   str       				=  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 		SCREAMING_SNAKE_CASE							(					) ->	int:
     UpperCamelCase__			:   List[Any]       				=  create_accelerator()
     UpperCamelCase__			:   Tuple       				=  torch.nn.Linear(1					,    1			)
     UpperCamelCase__			:   Dict       				=  accelerator.prepare(__lowerCAmelCase			)
     create_dataloader(__lowerCAmelCase					,    dataset_size=3					,    batch_size=1					,    iterable=__lowerCAmelCase			)

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

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







def 		SCREAMING_SNAKE_CASE							(					) ->	List[Any]:
     UpperCamelCase__			:   Tuple       				=  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"			)
     UpperCamelCase__			:   int       				=  accelerator.state.distributed_type
     UpperCamelCase__			:   int       				=  DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(__lowerCAmelCase			)
     UpperCamelCase__			:   str       				=  original_state


if __name__ == "__main__":
 main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)