



'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

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







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"swinv2"

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




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

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







'''simple docstring'''


lowerCAmelCase					:     Optional[int]												=256
# Modulus to hash a string
lowerCAmelCase					:     Tuple												=1_000_003
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					str ):
   lowercase_			:Dict    			= len(__lowerCamelCase )
   lowercase_			:int    			= len(__lowerCamelCase )
   if p_len > t_len:
      return False

   lowercase_			:Any    			= 0
   lowercase_			:Dict    			= 0
   lowercase_			:int    			= 1

   # Calculating the hash of pattern and substring of text
   for i in range(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      lowercase_			:Tuple    			= (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      lowercase_			:Tuple    			= (modulus_power * alphabet_size) % modulus

   for i in range(0	,t_len - p_len + 1 ):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      lowercase_			:Tuple    			= (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 							UpperCAmelCase_  (						):
   lowercase_			:List[Any]    			= "abc1abc12"
   lowercase_			:List[str]    			= "alskfjaldsabc1abc1abc12k23adsfabcabc"
   lowercase_			:int    			= "alskfjaldsk23adsfabcabc"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase ) and not rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 2)
   lowercase_			:Union[str, Any]    			= "ABABX"
   lowercase_			:Optional[Any]    			= "ABABZABABYABABX"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 3)
   lowercase_			:Dict    			= "AAAB"
   lowercase_			:int    			= "ABAAAAAB"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 4)
   lowercase_			:Tuple    			= "abcdabcy"
   lowercase_			:Union[str, Any]    			= "abcxabcdabxabcdabcdabcy"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 5)
   lowercase_			:Tuple    			= "Lü"
   lowercase_			:List[Any]    			= "Lüsai"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )
   lowercase_			:str    			= "Lue"
   assert not rabin_karp(__lowerCamelCase	,__lowerCamelCase )
   print("Success." )


if __name__ == "__main__":
 test_rabin_karp()


