


"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	get_failure_array(UpperCamelCase__ )

 # 2) Step through text searching for pattern
 __lowercase							,		__lowercase									=	0, 0  # index into text, pattern
 while i < len(UpperCamelCase__ ):
  if pattern[j] == text[i]:
   if j == (len(UpperCamelCase__ ) - 1):
    return True
   j += 1

  # if this is a prefix in our pattern
  # just go back far enough to continue
  elif j > 0:
   __lowercase									=	failure[j - 1]
   continue
  i += 1
 return False


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[0]
 __lowercase									=	0
 __lowercase									=	1
 while j < len(UpperCamelCase__ ):
  if pattern[i] == pattern[j]:
   i += 1
  elif i > 0:
   __lowercase									=	failure[i - 1]
   continue
  j += 1
  failure.append(UpperCamelCase__ )
 return failure


if __name__ == "__main__":
       # Test 1)
       UpperCAmelCase__  							="abc1abc12"
       UpperCAmelCase__  							="alskfjaldsabc1abc1abc12k23adsfabcabc"
       UpperCAmelCase__  							="alskfjaldsk23adsfabcabc"
       assert kmp(pattern, texta) and not kmp(pattern, texta)

       # Test 2)
       UpperCAmelCase__  							="ABABX"
       UpperCAmelCase__  							="ABABZABABYABABX"
       assert kmp(pattern, text)

       # Test 3)
       UpperCAmelCase__  							="AAAB"
       UpperCAmelCase__  							="ABAAAAAB"
       assert kmp(pattern, text)

       # Test 4)
       UpperCAmelCase__  							="abcdabcy"
       UpperCAmelCase__  							="abcxabcdabxabcdabcdabcy"
       assert kmp(pattern, text)

       # Test 5)
       UpperCAmelCase__  							="aabaabaaa"
       assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

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





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

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