"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A  =				logging.get_logger(__name__)

__A  =				{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class  _snake_case     (					a__				, a__		):
						snake_case__			  =			"bit"
						snake_case__			  =			["preactivation", "bottleneck"]
						snake_case__			  =			["SAME", "VALID"]






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			Any=64							,					UpperCAmelCase      :			Tuple=[256, 512, 1024, 2048]							,					UpperCAmelCase      :			Any=[3, 4, 6, 3]							,					UpperCAmelCase      :			Any="preactivation"							,					UpperCAmelCase      :			Any="relu"							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=32							,					UpperCAmelCase      :			List[str]=0.0							,					UpperCAmelCase      :			List[str]=False							,					UpperCAmelCase      :			Optional[int]=32							,					UpperCAmelCase      :			str=1							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Union[str, Any]=None							,					**UpperCAmelCase      :			int							,					):
													super().__init__(**UpperCAmelCase    )
													if layer_type not in self.layer_types:
																				raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types    )}"""    )
													if global_padding is not None:
																				if global_padding.upper() in self.supported_padding:
																											__lowerCamelCase					:		str		   =     global_padding.upper()
																				else:
																											raise ValueError(F"""Padding strategy {global_padding} not supported"""    )
													__lowerCamelCase					:		Tuple		   =     num_channels
													__lowerCamelCase					:		Optional[int]		   =     embedding_size
													__lowerCamelCase					:		str		   =     hidden_sizes
													__lowerCamelCase					:		Union[str, Any]		   =     depths
													__lowerCamelCase					:		Union[str, Any]		   =     layer_type
													__lowerCamelCase					:		str		   =     hidden_act
													__lowerCamelCase					:		Union[str, Any]		   =     global_padding
													__lowerCamelCase					:		List[str]		   =     num_groups
													__lowerCamelCase					:		Any		   =     drop_path_rate
													__lowerCamelCase					:		List[str]		   =     embedding_dynamic_padding
													__lowerCamelCase					:		List[str]		   =     output_stride
													__lowerCamelCase					:		List[str]		   =     width_factor

													__lowerCamelCase					:		Any		   =     ["stem"] + [F"""stage{idx}""" for idx in range(1							,					len(UpperCAmelCase    ) + 1    )]
													__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     get_aligned_output_features_output_indices(
													    out_features=UpperCAmelCase							,					out_indices=UpperCAmelCase							,					stage_names=self.stage_names    )
"""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()