

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


snake_case_   :   List[str]        =       4
snake_case_   :   int        =       3







class       snake_case__  ( _UpperCamelCase     ):
			pass



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					for shard in shards:
										for i in range(lowerCamelCase__	):
															yield {"i": i, "shard": shard}



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				int(os.environ["RANK"]	)
					UpperCAmelCase			:   Optional[Any]      =				int(os.environ["WORLD_SIZE"]	)

					UpperCAmelCase			:   Tuple      =				ArgumentParser()
					parser.add_argument("--streaming"							,      type=lowerCamelCase__	)
					parser.add_argument("--local_rank"							,      type=lowerCamelCase__	)
					parser.add_argument("--num_workers"							,      type=lowerCamelCase__							,      default=0	)
					UpperCAmelCase			:   Union[str, Any]      =				parser.parse_args()
					UpperCAmelCase			:   Optional[int]      =				args.streaming
					UpperCAmelCase			:   Union[str, Any]      =				args.num_workers

					UpperCAmelCase			:   Optional[Any]      =				{"shards": [F"""shard_{shard_idx}""" for shard_idx in range(lowerCamelCase__	)]}
					UpperCAmelCase			:   Optional[int]      =				IterableDataset.from_generator(lowerCamelCase__							,      gen_kwargs=lowerCamelCase__	)
					if not streaming:
										UpperCAmelCase			:   Any      =				Dataset.from_list(list(lowerCamelCase__	)	)

					UpperCAmelCase			:   List[Any]      =				split_dataset_by_node(lowerCamelCase__							,      rank=lowerCamelCase__							,      world_size=lowerCamelCase__	)
					UpperCAmelCase			:   Optional[Any]      =				torch.utils.data.DataLoader(lowerCamelCase__							,      num_workers=lowerCamelCase__	)

					UpperCAmelCase			:   List[Any]      =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
					UpperCAmelCase			:   str      =				full_size // world_size
					expected_local_size += int(rank < (full_size % world_size)	)

					UpperCAmelCase			:   Optional[int]      =				sum(1 for _ in dataloader	)
					if local_size != expected_local_size:
										raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
	main()





"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case_   :   Union[str, Any]        =       abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					config.addinivalue_line(
					    "markers"							,      "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"	)
					config.addinivalue_line(
					    "markers"							,      "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"	)
					config.addinivalue_line("markers"							,      "is_pipeline_test: mark test to run only when pipelines are tested"	)
					config.addinivalue_line("markers"							,      "is_staging_test: mark test to run only in the staging environment"	)
					config.addinivalue_line("markers"							,      "accelerate_tests: mark test that require accelerate"	)
					config.addinivalue_line("markers"							,      "tool_tests: mark the tool tests that are run on their specific schedule"	)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_terminal_summary_main

					UpperCAmelCase			:   Optional[int]      =				terminalreporter.config.getoption("--make-reports"	)
					if make_reports:
										pytest_terminal_summary_main(_lowercase							,      id=_lowercase	)



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if exitstatus == 5:
										UpperCAmelCase			:   List[str]      =				0


# Doctest custom flag to ignore output.
snake_case_   :   Union[str, Any]        =       doctest.register_optionflag("""IGNORE_RESULT""")

snake_case_   :   Optional[int]        =       doctest.OutputChecker







class       snake_case__  ( lowerCAmelCase_     ):







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Tuple			,  lowercase			: Optional[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								if IGNORE_RESULT & optionflags:
													return True
								return OutputChecker.check_output(self			,  lowercase			,  lowercase			,  lowercase  )

snake_case_   :   List[str]        =       CustomOutputChecker
snake_case_   :   Optional[Any]        =       HfDoctestModule




snake_case_   :   List[str]        =       HfDocTestParser


