

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


_lowerCamelCase				:   Dict					   =							logging.get_logger(__name__)

_lowerCamelCase				:   Any					   =							{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}





class 					__UpperCAmelCase     (       A_			,  A_						):
   UpperCamelCase        =     "focalnet"



   def __init__(							self							:     Any,		__A							:     Optional[int]=2_2_4,		__A							:     Tuple=4,		__A							:     Optional[Any]=3,		__A							:     Optional[Any]=9_6,		__A							:     List[Any]=False,		__A							:     Optional[int]=[1_9_2, 3_8_4, 7_6_8, 7_6_8],		__A							:     Optional[Any]=[2, 2, 6, 2],		__A							:     Optional[int]=[2, 2, 2, 2],		__A							:     List[str]=[3, 3, 3, 3],		__A							:     List[str]="gelu",		__A							:     Dict=4.0,		__A							:     str=0.0,		__A							:     Any=0.1,		__A							:     Dict=False,		__A							:     List[Any]=1E-4,		__A							:     Dict=False,		__A							:     Optional[int]=False,		__A							:     Optional[Any]=False,		__A							:     Dict=0.0_2,		__A							:     Optional[int]=1E-5,		__A							:     Optional[int]=3_2,		__A							:     Any=None,		__A							:     List[Any]=None,		**__A							:     List[Any],		):
     super().__init__(**_snake_case						)

     UpperCAmelCase       :							Optional[Any]  = image_size
     UpperCAmelCase       :							str  = patch_size
     UpperCAmelCase       :							Optional[Any]  = num_channels
     UpperCAmelCase       :							Dict  = embed_dim
     UpperCAmelCase       :							List[str]  = use_conv_embed
     UpperCAmelCase       :							Optional[Any]  = hidden_sizes
     UpperCAmelCase       :							Dict  = depths
     UpperCAmelCase       :							List[Any]  = focal_levels
     UpperCAmelCase       :							Tuple  = focal_windows
     UpperCAmelCase       :							str  = hidden_act
     UpperCAmelCase       :							Tuple  = mlp_ratio
     UpperCAmelCase       :							Tuple  = hidden_dropout_prob
     UpperCAmelCase       :							str  = drop_path_rate
     UpperCAmelCase       :							str  = use_layerscale
     UpperCAmelCase       :							List[Any]  = layerscale_value
     UpperCAmelCase       :							Optional[Any]  = use_post_layernorm
     UpperCAmelCase       :							str  = use_post_layernorm_in_modulation
     UpperCAmelCase       :							Dict  = normalize_modulator
     UpperCAmelCase       :							Dict  = initializer_range
     UpperCAmelCase       :							Union[str, Any]  = layer_norm_eps
     UpperCAmelCase       :							Any  = encoder_stride
     UpperCAmelCase       :							int  = ['''stem'''] + [F'''stage{idx}''' for idx in range(1,		len(self.depths						) + 1						)]
     UpperCAmelCase       :							Tuple  = get_aligned_output_features_output_indices(
         out_features=_snake_case,		out_indices=_snake_case,		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


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			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()
