




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


lowerCamelCase__			:       Optional[int]         =							logging.get_logger(__name__)

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





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "swinv2"

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



 def __init__(							self  :		List[str]    ,    _lowerCAmelCase  :		Dict=224    ,    _lowerCAmelCase  :		Tuple=4    ,    _lowerCAmelCase  :		str=3    ,    _lowerCAmelCase  :		Dict=96    ,    _lowerCAmelCase  :		List[Any]=[2, 2, 6, 2]    ,    _lowerCAmelCase  :		str=[3, 6, 12, 24]    ,    _lowerCAmelCase  :		Union[str, Any]=7    ,    _lowerCAmelCase  :		List[Any]=4.0    ,    _lowerCAmelCase  :		Optional[Any]=True    ,    _lowerCAmelCase  :		int=0.0    ,    _lowerCAmelCase  :		Tuple=0.0    ,    _lowerCAmelCase  :		Any=0.1    ,    _lowerCAmelCase  :		Optional[int]="gelu"    ,    _lowerCAmelCase  :		Dict=False    ,    _lowerCAmelCase  :		List[str]=0.02    ,    _lowerCAmelCase  :		Dict=1E-5    ,    _lowerCAmelCase  :		List[Any]=32    ,    **_lowerCAmelCase  :		Union[str, Any]    ,    ):
       super().__init__(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      patch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      embed_dim
       SCREAMING_SNAKE_CASE_     						=      depths
       SCREAMING_SNAKE_CASE_     						=      len(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      num_heads
       SCREAMING_SNAKE_CASE_     						=      window_size
       SCREAMING_SNAKE_CASE_     						=      mlp_ratio
       SCREAMING_SNAKE_CASE_     						=      qkv_bias
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      drop_path_rate
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      use_absolute_embeddings
       SCREAMING_SNAKE_CASE_     						=      layer_norm_eps
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      int(embed_dim * 2 ** (len(_lowerCAmelCase						) - 1)						)
       SCREAMING_SNAKE_CASE_     						=      (0, 0, 0, 0)





import random
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


if __name__ == "__main__":
   lowerCamelCase__			:       Union[str, Any]         =							[0, 1, 2, 3, 4, 5, 6, 7]
   lowerCamelCase__			:       Optional[int]         =							['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))