



"""simple docstring"""

# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     scheduler
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     optimizers if isinstance(_SCREAMING_SNAKE_CASE     ,       (list, tuple)    ) else [optimizers]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     split_batches
					SCREAMING_SNAKE_CASE						:							str							=     step_with_optimizer
					SCREAMING_SNAKE_CASE						:							Dict							=     GradientState()





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       *_SCREAMING_SNAKE_CASE				:       Dict     ,       **_SCREAMING_SNAKE_CASE				:       str    )						->	Optional[int]:





					"""simple docstring"""


					if not self.step_with_optimizer:
									# No link between scheduler and optimizer -> just step
									self.scheduler.step(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
									return

					# Otherwise, first make sure the optimizer was stepped.
					if not self.gradient_state.sync_gradients:
									if self.gradient_state.adjust_scheduler:
													self.scheduler._step_count += 1
									return

					for opt in self.optimizers:
									if opt.step_was_skipped:
													return
					if self.split_batches:
									# Split batches -> the training dataloader batch size is not changed so one step per training step
									self.scheduler.step(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					else:
									# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
									# num_processes steps per training step
									SCREAMING_SNAKE_CASE						:							Dict							=     AcceleratorState().num_processes
									for _ in range(_SCREAMING_SNAKE_CASE    ):
													# Special case when using OneCycle and `drop_last` was not used
													if hasattr(self.scheduler     ,       'total_steps'    ):
																	if self.scheduler._step_count <= self.scheduler.total_steps:
																					self.scheduler.step(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
													else:
																	self.scheduler.step(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					return self.scheduler.get_last_lr()





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					return self.scheduler.state_dict()





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	int:





					"""simple docstring"""


					self.scheduler.load_state_dict(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[str]:





					"""simple docstring"""


					return self.scheduler.get_lr()







	def 			_lowerCAmelCase     (      self				:       Tuple     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.scheduler.print_lr(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

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  __snake_case							(	__A      :						str     )     ->       Dict:






				'''simple docstring'''
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     'mock-s3-bucket'
				SCREAMING_SNAKE_CASE						:							Tuple							=     F"""s3://{mock_bucket}"""
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path.startswith('s3://'     ) is False

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     './local/path'
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path == new_dataset_path
def  __snake_case							(	__A      :						Tuple     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     is_remote_filesystem(__A     )
				assert is_remote is True

				SCREAMING_SNAKE_CASE						:							Dict							=     fsspec.filesystem('file'     )

				SCREAMING_SNAKE_CASE						:							Dict							=     is_remote_filesystem(__A     )
				assert is_remote is False
@pytest.mark.parametrize('compression_fs_class'							,		__A     )
def  __snake_case							(	__A      :						Any							,		__A      :						Optional[int]							,		__A      :						List[Any]							,		__A      :						Optional[Any]							,		__A      :						str							,		__A      :						Tuple							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
				SCREAMING_SNAKE_CASE						:							List[Any]							=     input_paths[compression_fs_class.protocol]
				if input_path is None:
								SCREAMING_SNAKE_CASE						:							str							=     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(__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     fsspec.filesystem(compression_fs_class.protocol							,		fo=__A     )
				assert isinstance(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     os.path.basename(__A     )
				SCREAMING_SNAKE_CASE						:							Dict							=     expected_filename[: expected_filename.rindex('.'     )]
				assert fs.glob('*'     ) == [expected_filename]
				with fs.open(__A							,		'r'							,		encoding='utf-8'     ) as f, open(__A							,		encoding='utf-8'     ) as expected_file:
								assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol'							,		['zip', 'gzip']     )
def  __snake_case							(	__A      :						Any							,		__A      :						int							,		__A      :						Union[str, Any]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[Any]							=     {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     compressed_file_paths[protocol]
				SCREAMING_SNAKE_CASE						:							str							=     'dataset.jsonl'
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     F"""{protocol}://{member_file_path}::{compressed_file_path}"""
				SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						:							Any							=     fsspec.get_fs_token_paths(__A     )
				assert fs.isfile(__A     )
				assert not fs.isfile('non_existing_' + member_file_path     )
@pytest.mark.integration
def  __snake_case							(	__A      :						List[str]							,		__A      :						int							,		__A      :						int							,		__A      :						Optional[int]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     hf_api.dataset_info(__A							,		token=__A     )
				SCREAMING_SNAKE_CASE						:							str							=     HfFileSystem(repo_info=__A							,		token=__A     )
				assert sorted(hffs.glob('*'     )     ) == [".gitattributes", "data"]
				assert hffs.isdir('data'     )
				assert hffs.isfile('.gitattributes'     ) and hffs.isfile('data/text_data.txt'     )
				with open(__A     ) as f:
								assert hffs.open('data/text_data.txt'							,		'r'     ).read() == f.read()







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






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     'bz2'

				# Import module
				import datasets.filesystems

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

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





