


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor

class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase):
     A:		Union[str, Any]			=      "pixel_values"
     A:		List[Any]			=      False
     A:		List[Any]			=      TimmBackboneConfig


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	**lowerCamelCase__							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      requires_backends(self							,	'''timm'''			)
      super().__init__(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   config

      if config.backbone is None:
       raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''			)

      if config.backbone not in timm.list_models():
       raise ValueError(F"backbone {config.backbone} is not supported by timm."			)

      if hasattr(lowerCamelCase__							,	'''out_features'''			) and config.out_features is not None:
       raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''			)

      UpperCamelCase__					:    List[Any]          =   getattr(lowerCamelCase__							,	'''use_pretrained_backbone'''							,	lowerCamelCase__			)
      if pretrained is None:
       raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''			)

      # We just take the final layer by default. This matches the default for the transformers models.
      UpperCamelCase__					:    Optional[int]          =   config.out_indices if getattr(lowerCamelCase__							,	'''out_indices'''							,	lowerCamelCase__			) is not None else (-1,)

      UpperCamelCase__					:    Union[str, Any]          =   timm.create_model(
          config.backbone							,	pretrained=lowerCamelCase__							,	features_only=config.features_only							,	in_chans=config.num_channels							,	out_indices=lowerCamelCase__							,	**lowerCamelCase__							,	)
      # These are used to control the output of the model when called. If output_hidden_states is True, then
      # return_layers is modified to include all layers.
      UpperCamelCase__					:    Dict          =   self._backbone.return_layers
      UpperCamelCase__					:    int          =   {layer['''module''']: str(lowerCamelCase__			) for i, layer in enumerate(self._backbone.feature_info.info			)}
      super()._init_backbone(lowerCamelCase__			)


     @classmethod
     def      UpperCAmelCase__	(      cls							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]							,	*lowerCamelCase__							:     str							,	**lowerCamelCase__							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






      requires_backends(cls							,	['''vision''', '''timm''']			)
      from ...models.timm_backbone import TimmBackboneConfig

      UpperCamelCase__					:    int          =   kwargs.pop('''config'''							,	TimmBackboneConfig()			)

      UpperCamelCase__					:    Optional[int]          =   kwargs.pop('''use_timm_backbone'''							,	lowerCamelCase__			)
      if not use_timm:
       raise ValueError('''use_timm_backbone must be True for timm backbones'''			)

      UpperCamelCase__					:    Optional[int]          =   kwargs.pop('''num_channels'''							,	config.num_channels			)
      UpperCamelCase__					:    int          =   kwargs.pop('''features_only'''							,	config.features_only			)
      UpperCamelCase__					:    Optional[int]          =   kwargs.pop('''use_pretrained_backbone'''							,	config.use_pretrained_backbone			)
      UpperCamelCase__					:    List[Any]          =   kwargs.pop('''out_indices'''							,	config.out_indices			)
      UpperCamelCase__					:    Optional[int]          =   TimmBackboneConfig(
          backbone=lowerCamelCase__							,	num_channels=lowerCamelCase__							,	features_only=lowerCamelCase__							,	use_pretrained_backbone=lowerCamelCase__							,	out_indices=lowerCamelCase__							,	)
      return super()._from_config(lowerCamelCase__							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      pass


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     List[str]=None							,	lowerCamelCase__							:     Optional[Any]=None							,	lowerCamelCase__							:     Union[str, Any]=None							,	**lowerCamelCase__							:     List[str]			)  ->      Union[BackboneOutput, Tuple[Tensor, ...]]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   return_dict if return_dict is not None else self.config.use_return_dict
      UpperCamelCase__					:    Any          =   (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      UpperCamelCase__					:    Union[str, Any]          =   output_attentions if output_attentions is not None else self.config.output_attentions

      if output_attentions:
       raise ValueError('''Cannot output attentions for timm backbones at the moment'''			)

      if output_hidden_states:
       # We modify the return layers to include all the stages of the backbone
       UpperCamelCase__					:    Union[str, Any]          =   self._all_layers
       UpperCamelCase__					:    List[str]          =   self._backbone(lowerCamelCase__							,	**lowerCamelCase__			)
       UpperCamelCase__					:    Optional[int]          =   self._return_layers
       UpperCamelCase__					:    Optional[int]          =   tuple(hidden_states[i] for i in self.out_indices			)
      else:
       UpperCamelCase__					:    Any          =   self._backbone(lowerCamelCase__							,	**lowerCamelCase__			)
       UpperCamelCase__					:    Dict          =   None

      UpperCamelCase__					:    Optional[int]          =   tuple(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   tuple(lowerCamelCase__			) if hidden_states is not None else None

      if not return_dict:
       UpperCamelCase__					:    str          =   (feature_maps,)
       if output_hidden_states:
        UpperCamelCase__					:    Dict          =   output + (hidden_states,)
       return output

      return BackboneOutput(feature_maps=lowerCamelCase__							,	hidden_states=lowerCamelCase__							,	attentions=lowerCamelCase__			)






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (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__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


