"""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_			(	lowerCAmelCase    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	data

				def __iter__( self			):






										"""simple docstring"""

										for element in self.data:
																yield element





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]=True    )    -> Union[str, Any]:





						"""simple docstring"""






						snake_case	    =	Accelerator(even_batches=_snake_case    )
						assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
						return accelerator





def       lowerCAmelCase__			(	_UpperCamelCase				: Accelerator       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: bool = False    )    -> str:





						"""simple docstring"""






						if iterable:
												snake_case	    =	DummyIterableDataset(torch.as_tensor(range(_snake_case    )    )    )
						else:
												snake_case	    =	TensorDataset(torch.as_tensor(range(_snake_case    )    )    )

						snake_case	    =	DataLoader(_snake_case       ,	batch_size=_snake_case    )
						snake_case	    =	accelerator.prepare(_snake_case    )

						return dl





def       lowerCAmelCase__			(	_UpperCamelCase				: Accelerator       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: List[int]       ,	_UpperCamelCase				: List[int]       ,	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	create_dataloader(accelerator=_snake_case       ,	dataset_size=_snake_case       ,	batch_size=_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       lowerCAmelCase__			(	)    -> List[str]:





						"""simple docstring"""






						snake_case	    =	create_accelerator()

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





def       lowerCAmelCase__			(	)    -> Union[str, Any]:





						"""simple docstring"""






						snake_case	    =	create_accelerator(even_batches=_snake_case    )

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





def       lowerCAmelCase__			(	)    -> Tuple:





						"""simple docstring"""






						snake_case	    =	create_accelerator(even_batches=_snake_case    )

						snake_case	    =	torch.nn.Linear(1       ,	1    )
						snake_case	    =	accelerator.prepare(_snake_case    )

						snake_case	    =	create_dataloader(_snake_case       ,	dataset_size=3       ,	batch_size=1    )

						snake_case	    =	[]
						with accelerator.join_uneven_inputs([ddp_model]    ):
												for batch_idx, batch in enumerate(_snake_case    ):
																		snake_case	    =	ddp_model(batch[0].float()    )
																		snake_case	    =	output.sum()
																		loss.backward()
																		batch_idxs.append(_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       lowerCAmelCase__			(	_UpperCamelCase				: str    )    -> str:





						"""simple docstring"""






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

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





def       lowerCAmelCase__			(	)    -> Optional[Any]:





						"""simple docstring"""






						snake_case	    =	True
						snake_case	    =	False
						snake_case	    =	create_accelerator(even_batches=_snake_case    )
						snake_case	    =	torch.nn.Linear(1       ,	1    )
						snake_case	    =	accelerator.prepare(_snake_case    )
						snake_case	    =	create_dataloader(_snake_case       ,	dataset_size=3       ,	batch_size=1    )
						snake_case	    =	create_dataloader(_snake_case       ,	dataset_size=3       ,	batch_size=1    )

						with accelerator.join_uneven_inputs([ddp_model]       ,	even_batches=_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       lowerCAmelCase__			(	)    -> List[str]:





						"""simple docstring"""






						snake_case	    =	True
						snake_case	    =	False
						snake_case	    =	create_accelerator(even_batches=_snake_case    )
						snake_case	    =	torch.nn.Linear(1       ,	1    )
						snake_case	    =	accelerator.prepare(_snake_case    )
						create_dataloader(_snake_case       ,	dataset_size=3       ,	batch_size=1       ,	iterable=_snake_case    )
						snake_case	    =	create_dataloader(_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=_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       lowerCAmelCase__			(	)    -> str:





						"""simple docstring"""






						snake_case	    =	create_accelerator()
						snake_case	    =	torch.nn.Linear(1       ,	1    )
						snake_case	    =	accelerator.prepare(_snake_case    )
						create_dataloader(_snake_case       ,	dataset_size=3       ,	batch_size=1       ,	iterable=_snake_case    )

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

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





def       lowerCAmelCase__			(	)    -> List[str]:





						"""simple docstring"""






						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(_snake_case    )
						snake_case	    =	original_state


if __name__ == "__main__":
					main()




import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
		if compression_fs_class.protocol == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_fs_class.protocol == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

	# Overwrite protocol and reload
	register_implementation(_snake_case      ,   _snake_case      ,   clobber=_snake_case   )
	with pytest.warns(_snake_case   ) as warning_info:
		importlib.reload(datasets.filesystems   )

	assert len(_snake_case   ) == 1
	assert (
	    str(warning_info[0].message   )
	    == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
	)
