



"""simple docstring"""







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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[Any]							  =     "dinat"

			A__	:					Optional[Any]							  =     {
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=[3, 4, 6, 5]						,	SCREAMING_SNAKE_CASE__=[2, 4, 8, 16]						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]						,	SCREAMING_SNAKE_CASE__=3.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Union[str, Any]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

				A__											=					patch_size
				A__											=					num_channels
				A__											=					embed_dim
				A__											=					depths
				A__											=					len(SCREAMING_SNAKE_CASE__		)
				A__											=					num_heads
				A__											=					kernel_size
				A__											=					dilations
				A__											=					mlp_ratio
				A__											=					qkv_bias
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					drop_path_rate
				A__											=					hidden_act
				A__											=					layer_norm_eps
				A__											=					initializer_range
				# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				A__											=					int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__		) - 1)		)
				A__											=					layer_scale_init_value
				A__											=					["stem"] + [f"""stage{idx}""" for idx in range(1						,	len(SCREAMING_SNAKE_CASE__		) + 1		)]
				A__							,							A__											=					get_aligned_output_features_output_indices(
				    out_features=SCREAMING_SNAKE_CASE__						,	out_indices=SCREAMING_SNAKE_CASE__						,	stage_names=self.stage_names		)






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


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


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


def    a      ( )     ->List[str]:




			'''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=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						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(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			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()