


"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A     :   str			    =			logging.get_logger(__name__)

A     :   Union[str, Any]			    =			{
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}
class   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      ="""swin"""

							__UpperCAmelCase						:    Any				      ={
							    """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=0.0_2    ,   __a=1e-5    ,   __a=32    ,   __a=None    ,   __a=None    ,   **__a    ,   ):
										super().__init__(**__a	)

										__lowerCAmelCase				    =			image_size
										__lowerCAmelCase				    =			patch_size
										__lowerCAmelCase				    =			num_channels
										__lowerCAmelCase				    =			embed_dim
										__lowerCAmelCase				    =			depths
										__lowerCAmelCase				    =			len(__a	)
										__lowerCAmelCase				    =			num_heads
										__lowerCAmelCase				    =			window_size
										__lowerCAmelCase				    =			mlp_ratio
										__lowerCAmelCase				    =			qkv_bias
										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			drop_path_rate
										__lowerCAmelCase				    =			hidden_act
										__lowerCAmelCase				    =			use_absolute_embeddings
										__lowerCAmelCase				    =			layer_norm_eps
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			encoder_stride
										# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										__lowerCAmelCase				    =			int(embed_dim * 2 ** (len(__a	) - 1)	)
										__lowerCAmelCase				    =			["stem"] + [f"stage{idx}" for idx in range(1    ,   len(__a	) + 1	)]
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			get_aligned_output_features_output_indices(
										    out_features=__a    ,   out_indices=__a    ,   stage_names=self.stage_names	)
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      =version.parse("""1.11"""		)

							@property
							def 			snake_case				(     self	):
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]	)



							@property
							def 			snake_case				(     self	):
										return 1e-4











'''simple docstring'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (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
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
