"""simple docstring"""





import os
import sys

__A  =				os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__A  =				[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]


@add_start_docstrings(AutoConfig.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	Optional[Any]						,   **_lowerCamelCase:	List[str]      ) ->      Any:



							'''simple docstring'''

							return AutoConfig.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )


@add_start_docstrings(AutoTokenizer.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	Optional[Any]						,   **_lowerCamelCase:	Optional[Any]      ) ->      int:



							'''simple docstring'''

							return AutoTokenizer.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )


@add_start_docstrings(AutoModel.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	Optional[Any]						,   **_lowerCamelCase:	List[Any]      ) ->      str:



							'''simple docstring'''

							return AutoModel.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )


@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	str						,   **_lowerCamelCase:	List[Any]      ) ->      List[Any]:



							'''simple docstring'''

							return AutoModelForCausalLM.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )


@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	List[Any]						,   **_lowerCamelCase:	int      ) ->      int:



							'''simple docstring'''

							return AutoModelForMaskedLM.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )


@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	Dict						,   **_lowerCamelCase:	List[Any]      ) ->      int:



							'''simple docstring'''

							return AutoModelForSequenceClassification.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def    lowercase_   (   *_lowerCamelCase:	str						,   **_lowerCamelCase:	Any      ) ->      Optional[Any]:



							'''simple docstring'''

							return AutoModelForQuestionAnswering.from_pretrained(*_lowerCamelCase						,   **_lowerCamelCase      )
"""simple docstring"""





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


__A  =				4
__A  =				3
class  _snake_case     (					a__		):
						pass


def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

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


def    lowercase_   (   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     int(os.environ["RANK"]      )
							__lowerCamelCase					:		Optional[int]		   =     int(os.environ["WORLD_SIZE"]      )

							__lowerCamelCase					:		Any		   =     ArgumentParser()
							parser.add_argument("--streaming"						,   type=_lowerCamelCase      )
							parser.add_argument("--local_rank"						,   type=_lowerCamelCase      )
							parser.add_argument("--num_workers"						,   type=_lowerCamelCase						,   default=0      )
							__lowerCamelCase					:		Dict		   =     parser.parse_args()
							__lowerCamelCase					:		str		   =     args.streaming
							__lowerCamelCase					:		List[Any]		   =     args.num_workers

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

							__lowerCamelCase					:		Union[str, Any]		   =     split_dataset_by_node(_lowerCamelCase						,   rank=_lowerCamelCase						,   world_size=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     torch.utils.data.DataLoader(_lowerCamelCase						,   num_workers=_lowerCamelCase      )

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

							__lowerCamelCase					:		Optional[Any]		   =     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()