






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      :       str				  =   4
__a      :       List[str]				  =   3


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     pass



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]    )						->      List[Any]:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( )						->      List[str]:





    """simple docstring"""

    __A         =	int(os.environ["""RANK"""]    )
    __A         =	int(os.environ["""WORLD_SIZE"""]    )

    __A         =	ArgumentParser()
    parser.add_argument("""--streaming"""			,				type=__lowercase    )
    parser.add_argument("""--local_rank"""			,				type=__lowercase    )
    parser.add_argument("""--num_workers"""			,				type=__lowercase			,				default=0    )
    __A         =	parser.parse_args()
    __A         =	args.streaming
    __A         =	args.num_workers

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

    __A         =	split_dataset_by_node(__lowercase			,				rank=__lowercase			,				world_size=__lowercase    )
    __A         =	torch.utils.data.DataLoader(__lowercase			,				num_workers=__lowercase    )

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

    __A         =	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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




