"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A  =				logging.get_logger(__name__)

__A  =				{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class  _snake_case     (					a__		):
						snake_case__			  =			"trocr"
						snake_case__			  =			["past_key_values"]
						snake_case__			  =			{
						    "num_attention_heads": "decoder_attention_heads",
						    "hidden_size": "d_model",
						    "num_hidden_layers": "decoder_layers",
						}






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]=50265							,					UpperCAmelCase      :			Optional[int]=1024							,					UpperCAmelCase      :			List[str]=12							,					UpperCAmelCase      :			Any=16							,					UpperCAmelCase      :			Tuple=4096							,					UpperCAmelCase      :			Dict="gelu"							,					UpperCAmelCase      :			int=512							,					UpperCAmelCase      :			int=0.1							,					UpperCAmelCase      :			Union[str, Any]=0.0							,					UpperCAmelCase      :			List[Any]=0.0							,					UpperCAmelCase      :			Optional[Any]=2							,					UpperCAmelCase      :			int=0.0_2							,					UpperCAmelCase      :			str=0.0							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			int=False							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			List[str]=1							,					UpperCAmelCase      :			Union[str, Any]=0							,					UpperCAmelCase      :			Tuple=2							,					**UpperCAmelCase      :			List[Any]							,					):
													__lowerCamelCase					:		Optional[Any]		   =     vocab_size
													__lowerCamelCase					:		List[str]		   =     d_model
													__lowerCamelCase					:		Union[str, Any]		   =     decoder_layers
													__lowerCamelCase					:		Optional[Any]		   =     decoder_attention_heads
													__lowerCamelCase					:		Optional[Any]		   =     decoder_ffn_dim
													__lowerCamelCase					:		Optional[int]		   =     activation_function
													__lowerCamelCase					:		Optional[int]		   =     max_position_embeddings
													__lowerCamelCase					:		Dict		   =     dropout
													__lowerCamelCase					:		str		   =     attention_dropout
													__lowerCamelCase					:		List[Any]		   =     activation_dropout
													__lowerCamelCase					:		Optional[Any]		   =     init_std
													__lowerCamelCase					:		Union[str, Any]		   =     decoder_layerdrop
													__lowerCamelCase					:		Dict		   =     use_cache
													__lowerCamelCase					:		List[Any]		   =     scale_embedding
													__lowerCamelCase					:		str		   =     use_learned_position_embeddings
													__lowerCamelCase					:		Union[str, Any]		   =     layernorm_embedding

													super().__init__(
													    pad_token_id=UpperCAmelCase							,					bos_token_id=UpperCAmelCase							,					eos_token_id=UpperCAmelCase							,					decoder_start_token_id=UpperCAmelCase							,					**UpperCAmelCase							,					)
"""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()