



'''simple docstring'''






# 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__( lowerCamelCase__     ):







    def __init__(				self			:  Tuple						,      __snake_case			:  int     ):
      a						: int					       =					data







    def __iter__(				self			:  List[str]     ):
      for element in self.data:
        yield element
def 				lowerCamelCase__	(						_A=True    ):
  a						: int					       =					Accelerator(even_batches=_A    )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A = False    ):
  if iterable:
    a						: Optional[int]					       =					DummyIterableDataset(torch.as_tensor(range(_A    )    )    )
  else:
    a						: Tuple					       =					TensorDataset(torch.as_tensor(range(_A    )    )    )

  a						: List[Any]					       =					DataLoader(_A			,      batch_size=_A    )
  a						: int					       =					accelerator.prepare(_A    )

  return dl
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A			,      ):
  a						: Tuple					       =					create_dataloader(accelerator=_A			,      dataset_size=_A			,      batch_size=_A    )

  a						: List[str]					       =					[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 				lowerCamelCase__	(						):
  a						: Tuple					       =					create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      _A			,      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(
      _A			,      dataset_size=7			,      batch_size=2			,      process_0_expected_batch_sizes=[2, 2]			,      process_1_expected_batch_sizes=[2, 2]			,      )
def 				lowerCamelCase__	(						):
  a						: List[str]					       =					create_accelerator(even_batches=_A    )

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

  verify_dataloader_batch_sizes(
      _A			,      dataset_size=7			,      batch_size=2			,      process_0_expected_batch_sizes=[2, 2]			,      process_1_expected_batch_sizes=[2, 1]			,      )
def 				lowerCamelCase__	(						):
  a						: List[Any]					       =					create_accelerator(even_batches=_A    )

  a						: str					       =					torch.nn.Linear(1			,      1    )
  a						: Any					       =					accelerator.prepare(_A    )

  a						: List[Any]					       =					create_dataloader(_A			,      dataset_size=3			,      batch_size=1    )

  a						: List[Any]					       =					[]
  with accelerator.join_uneven_inputs([ddp_model]    ):
    for batch_idx, batch in enumerate(_A    ):
      a						: List[Any]					       =					ddp_model(batch[0].float()    )
      a						: Optional[int]					       =					output.sum()
      loss.backward()
      batch_idxs.append(_A    )

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]
def 				lowerCamelCase__	(						_A    ):
  with warnings.catch_warnings(record=_A    ) as w:
    with accelerator.join_uneven_inputs([Mock()]    ):
      pass

    assert issubclass(w[-1].category			,      _A    )
    assert "only supported for multi-GPU" in str(w[-1].message    )
def 				lowerCamelCase__	(						):
  a						: Optional[Any]					       =					True
  a						: List[str]					       =					False
  a						: Tuple					       =					create_accelerator(even_batches=_A    )
  a						: Optional[Any]					       =					torch.nn.Linear(1			,      1    )
  a						: List[Any]					       =					accelerator.prepare(_A    )
  a						: Optional[int]					       =					create_dataloader(_A			,      dataset_size=3			,      batch_size=1    )
  a						: Dict					       =					create_dataloader(_A			,      dataset_size=3			,      batch_size=1    )

  with accelerator.join_uneven_inputs([ddp_model]			,      even_batches=_A    ):
    a						: str					       =					train_dl.batch_sampler.even_batches
    a						: Optional[Any]					       =					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 				lowerCamelCase__	(						):
  a						: int					       =					True
  a						: int					       =					False
  a						: Union[str, Any]					       =					create_accelerator(even_batches=_A    )
  a						: List[Any]					       =					torch.nn.Linear(1			,      1    )
  a						: Optional[int]					       =					accelerator.prepare(_A    )
  create_dataloader(_A			,      dataset_size=3			,      batch_size=1			,      iterable=_A    )
  a						: List[str]					       =					create_dataloader(_A			,      dataset_size=3			,      batch_size=1    )

  with warnings.catch_warnings():
    warnings.filterwarnings('ignore'    )
    try:
      with accelerator.join_uneven_inputs([ddp_model]			,      even_batches=_A    ):
        a						: Any					       =					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 				lowerCamelCase__	(						):
  a						: Dict					       =					create_accelerator()
  a						: List[Any]					       =					torch.nn.Linear(1			,      1    )
  a						: Tuple					       =					accelerator.prepare(_A    )
  create_dataloader(_A			,      dataset_size=3			,      batch_size=1			,      iterable=_A    )

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

    assert issubclass(w[-1].category			,      _A    )
    assert "only supported for map-style datasets" in str(w[-1].message    )
def 				lowerCamelCase__	(						):
  a						: Optional[Any]					       =					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						: Any					       =					accelerator.state.distributed_type
  a						: Dict					       =					DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(_A    )
  a						: str					       =					original_state


if __name__ == "__main__":
     main()




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output