





"""simple docstring"""





from typing import List

import numpy as np


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	{key: len(_SCREAMING_SNAKE_CASE    ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )}
		if len(set(lists_lengths.values()    )    ) > 1:
				raise RuntimeError(
				    (
				        "Sharding is ambiguous for this dataset: "
				        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
				        + "\n".join(F"\t- key {key} has length {length}" for key, length in lists_lengths.items()    )
				        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
				        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
				    )    )
		UpperCamelCase   =	max(lists_lengths.values()  ,	default=0    )
		return max(1  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		for group_idx in range(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				UpperCamelCase   =	shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				UpperCamelCase   =	range(_SCREAMING_SNAKE_CASE  ,	start + num_shards_to_add    )
				shards_indices_per_group.append(_SCREAMING_SNAKE_CASE    )
		return shards_indices_per_group


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE    )
		if num_shards == 1:
				return [dict(_SCREAMING_SNAKE_CASE    )]
		else:
				UpperCamelCase   =	_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE  ,	max_num_jobs=_SCREAMING_SNAKE_CASE    )
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(_SCREAMING_SNAKE_CASE    )    )
				]


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key]  ,	_SCREAMING_SNAKE_CASE    )
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}






def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	{len(_SCREAMING_SNAKE_CASE    ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )}
		UpperCamelCase   =	{}
		for size in list_sizes:
				UpperCamelCase   =	list(range(_SCREAMING_SNAKE_CASE    )    )
				rng.shuffle(indices_per_size[size]    )
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		UpperCamelCase   =	dict(_SCREAMING_SNAKE_CASE    )
		for key, value in shuffled_kwargs.items():
				if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE    )]]
		return shuffled_kwargs













"""simple docstring"""





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


lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"swinv2"

						UpperCAmelCase_ =			{
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}


						def __init__(self   ,							__a=2_24   ,							__a=4   ,							__a=3   ,							__a=96   ,							__a=[2, 2, 6, 2]   ,							__a=[3, 6, 12, 24]   ,							__a=7   ,							__a=4.0   ,							__a=True   ,							__a=0.0   ,							__a=0.0   ,							__a=0.1   ,							__a="gelu"   ,							__a=False   ,							__a=0.02   ,							__a=1e-5   ,							__a=32   ,							**__a   ,							) ->   Optional[Any]:
								super().__init__(**__a		)

								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	embed_dim
								UpperCamelCase   =	depths
								UpperCamelCase   =	len(__a		)
								UpperCamelCase   =	num_heads
								UpperCamelCase   =	window_size
								UpperCamelCase   =	mlp_ratio
								UpperCamelCase   =	qkv_bias
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	drop_path_rate
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	use_absolute_embeddings
								UpperCamelCase   =	layer_norm_eps
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCamelCase   =	int(embed_dim * 2 ** (len(__a		) - 1)		)
								UpperCamelCase   =	(0, 0, 0, 0)






