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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

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



class  _lowerCamelCase			(a__     ):
				_snake_case     =  """swinv2"""

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







				def __init__( self    :       str			,					lowerCamelCase_    :       Dict=2_2_4			,					lowerCamelCase_    :       List[Any]=4			,					lowerCamelCase_    :       Optional[Any]=3			,					lowerCamelCase_    :       List[Any]=9_6			,					lowerCamelCase_    :       Dict=[2, 2, 6, 2]			,					lowerCamelCase_    :       Dict=[3, 6, 1_2, 2_4]			,					lowerCamelCase_    :       Any=7			,					lowerCamelCase_    :       Tuple=4.0			,					lowerCamelCase_    :       List[str]=True			,					lowerCamelCase_    :       Any=0.0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       Dict=0.1			,					lowerCamelCase_    :       str="gelu"			,					lowerCamelCase_    :       str=False			,					lowerCamelCase_    :       Optional[Any]=0.02			,					lowerCamelCase_    :       Union[str, Any]=1E-5			,					lowerCamelCase_    :       Tuple=3_2			,					**lowerCamelCase_    :       Optional[Any]			,					):

					"""simple docstring"""






					super().__init__(**lowerCAmelCase__						)

					_lowercase :    Optional[Any]							=							image_size
					_lowercase :    Any							=							patch_size
					_lowercase :    int							=							num_channels
					_lowercase :    Any							=							embed_dim
					_lowercase :    Union[str, Any]							=							depths
					_lowercase :    Dict							=							len(lowerCAmelCase__						)
					_lowercase :    int							=							num_heads
					_lowercase :    int							=							window_size
					_lowercase :    List[str]							=							mlp_ratio
					_lowercase :    Dict							=							qkv_bias
					_lowercase :    Tuple							=							hidden_dropout_prob
					_lowercase :    str							=							attention_probs_dropout_prob
					_lowercase :    Any							=							drop_path_rate
					_lowercase :    int							=							hidden_act
					_lowercase :    List[str]							=							use_absolute_embeddings
					_lowercase :    str							=							layer_norm_eps
					_lowercase :    int							=							initializer_range
					_lowercase :    Tuple							=							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
					_lowercase :    Any							=							int(embed_dim * 2 ** (len(lowerCAmelCase__						) - 1)						)
					_lowercase :    Dict							=							(0, 0, 0, 0)




"""simple docstring"""







from __future__ import annotations

import collections
import pprint
from pathlib import Path


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return "".join(sorted(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return word_by_signature[signature(__UpperCAmelCase			)]


SCREAMING_SNAKE_CASE									=						Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE									=						sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE									=						collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



