






'''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    _lowerCAmelCase			(		lowercase__      ):



				"""simple docstring"""



				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       data



				def __iter__(      self    :    Optional[int]):






											'''simple docstring'''







											for element in self.data:
																		yield element


def _UpperCAmelCase   (	a						:	List[str]=True ):
							snake_case__	  =       Accelerator(even_batches=__SCREAMING_SNAKE_CASE )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Union[str, Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] = False ):
							if iterable:
														snake_case__	  =       DummyIterableDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE ) ) )
							else:
														snake_case__	  =       TensorDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE ) ) )

							snake_case__	  =       DataLoader(__SCREAMING_SNAKE_CASE  ,					batch_size=__SCREAMING_SNAKE_CASE )
							snake_case__	  =       accelerator.prepare(__SCREAMING_SNAKE_CASE )

							return dl

def _UpperCAmelCase   (	a						:	str  ,					a						:	Any  ,					a						:	List[Any]  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any]  ,					):
							snake_case__	  =       create_dataloader(accelerator=__SCREAMING_SNAKE_CASE  ,					dataset_size=__SCREAMING_SNAKE_CASE  ,					batch_size=__SCREAMING_SNAKE_CASE )

							snake_case__	  =       [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 _UpperCAmelCase   (	):
							snake_case__	  =       create_accelerator()

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

def _UpperCAmelCase   (	):
							snake_case__	  =       create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )

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

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

def _UpperCAmelCase   (	):
							snake_case__	  =       create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )

							snake_case__	  =       torch.nn.Linear(1  ,					1 )
							snake_case__	  =       accelerator.prepare(__SCREAMING_SNAKE_CASE )

							snake_case__	  =       create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1 )

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

							accelerator.wait_for_everyone()

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

def _UpperCAmelCase   (	a						:	List[str] ):
							with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE ) as w:
														with accelerator.join_uneven_inputs([Mock()] ):
																					pass

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

def _UpperCAmelCase   (	):
							snake_case__	  =       True
							snake_case__	  =       False
							snake_case__	  =       create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )
							snake_case__	  =       torch.nn.Linear(1  ,					1 )
							snake_case__	  =       accelerator.prepare(__SCREAMING_SNAKE_CASE )
							snake_case__	  =       create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1 )
							snake_case__	  =       create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1 )

							with accelerator.join_uneven_inputs([ddp_model]  ,					even_batches=__SCREAMING_SNAKE_CASE ):
														snake_case__	  =       train_dl.batch_sampler.even_batches
														snake_case__	  =       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 _UpperCAmelCase   (	):
							snake_case__	  =       True
							snake_case__	  =       False
							snake_case__	  =       create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )
							snake_case__	  =       torch.nn.Linear(1  ,					1 )
							snake_case__	  =       accelerator.prepare(__SCREAMING_SNAKE_CASE )
							create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1  ,					iterable=__SCREAMING_SNAKE_CASE )
							snake_case__	  =       create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1 )

							with warnings.catch_warnings():
														warnings.filterwarnings("""ignore""" )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]  ,					even_batches=__SCREAMING_SNAKE_CASE ):
																												snake_case__	  =       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 _UpperCAmelCase   (	):
							snake_case__	  =       create_accelerator()
							snake_case__	  =       torch.nn.Linear(1  ,					1 )
							snake_case__	  =       accelerator.prepare(__SCREAMING_SNAKE_CASE )
							create_dataloader(__SCREAMING_SNAKE_CASE  ,					dataset_size=3  ,					batch_size=1  ,					iterable=__SCREAMING_SNAKE_CASE )

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

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

def _UpperCAmelCase   (	):
							snake_case__	  =       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""" )
							snake_case__	  =       accelerator.state.distributed_type
							snake_case__	  =       DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(__SCREAMING_SNAKE_CASE )
							snake_case__	  =       original_state


if __name__ == "__main__":
	main()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
