

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


UpperCamelCase__  :      Optional[int]			=    4
UpperCamelCase__  :      int			=    3



class       __snake_case  (    lowerCAmelCase__       ):
		pass






def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





				"""simple docstring"""


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


def 					_UpperCAmelCase       (  ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						int(os.environ['RANK']       )
				SCREAMING_SNAKE_CASE_									=						int(os.environ['WORLD_SIZE']       )

				SCREAMING_SNAKE_CASE_									=						ArgumentParser()
				parser.add_argument('--streaming' ,   type=_SCREAMING_SNAKE_CASE       )
				parser.add_argument('--local_rank' ,   type=_SCREAMING_SNAKE_CASE       )
				parser.add_argument('--num_workers' ,   type=_SCREAMING_SNAKE_CASE ,   default=0       )
				SCREAMING_SNAKE_CASE_									=						parser.parse_args()
				SCREAMING_SNAKE_CASE_									=						args.streaming
				SCREAMING_SNAKE_CASE_									=						args.num_workers

				SCREAMING_SNAKE_CASE_									=						{'shards': [f"""shard_{shard_idx}""" for shard_idx in range(_SCREAMING_SNAKE_CASE       )]}
				SCREAMING_SNAKE_CASE_									=						IterableDataset.from_generator(_SCREAMING_SNAKE_CASE ,   gen_kwargs=_SCREAMING_SNAKE_CASE       )
				if not streaming:
								SCREAMING_SNAKE_CASE_									=						Dataset.from_list(list(_SCREAMING_SNAKE_CASE       )       )

				SCREAMING_SNAKE_CASE_									=						split_dataset_by_node(_SCREAMING_SNAKE_CASE ,   rank=_SCREAMING_SNAKE_CASE ,   world_size=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE_									=						torch.utils.data.DataLoader(_SCREAMING_SNAKE_CASE ,   num_workers=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE_									=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
				SCREAMING_SNAKE_CASE_									=						full_size // world_size
				expected_local_size += int(rank < (full_size % world_size)       )

				SCREAMING_SNAKE_CASE_									=						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()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


