




"""simple docstring"""


from typing import Dict

from .base import GenericTensor, Pipeline






class 					lowercase(     UpperCAmelCase__ ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Dict,      a_:		List[str]=None,      a_:		str=None,      a_:		Any=None,      **a_:		int		):




   '''simple docstring'''



   if tokenize_kwargs is None:
     _snake_case							:		Tuple								=  {}

   if truncation is not None:
     if "truncation" in tokenize_kwargs:
       raise ValueError(
           """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""		)
     _snake_case							:		Union[str, Any]								=  truncation

   _snake_case							:		List[str]								=  tokenize_kwargs

   _snake_case							:		Any								=  {}
   if return_tensors is not None:
     _snake_case							:		List[str]								=  return_tensors

   return preprocess_params, {}, postprocess_params


 def  UpperCamelCase_   (				self:		Tuple,      a_:		Union[str, Any],      **a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.framework
   _snake_case							:		int								=  self.tokenizer(a_,      return_tensors=a_,      **a_		)
   return model_inputs


 def  UpperCamelCase_   (				self:		List[Any],      a_:		Any		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.model(**a_		)
   return model_outputs


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		int,      a_:		Any=False		):




   '''simple docstring'''



   if return_tensors:
     return model_outputs[0]
   if self.framework == "pt":
     return model_outputs[0].tolist()
   elif self.framework == "tf":
     return model_outputs[0].numpy().tolist()


 def __call__(				self:		List[str],      *a_:		Optional[Any],      **a_:		List[Any]		):




   '''simple docstring'''



   return super().__call__(*a_,      **a_		)






'''simple docstring'''







from __future__ import annotations







class A__       :






      def __init__( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:str	)       ->					Optional[int]:




             '''simple docstring'''
             _a					,    _a    : List[str]     					=text, pattern
             _a					,    _a    : Union[str, Any]     					=len(SCREAMING_SNAKE_CASE	), len(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if char == self.pattern[i]:
                           return i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:int	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if self.pattern[i] != self.text[current_pos + i]:
                           return current_pos + i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					list[int]:




             '''simple docstring'''
             # searches pattern in text and returns index positions
             _a    : Union[str, Any]     					=[]
             for i in range(self.textLen - self.patLen + 1	):
                    _a    : Any     					=self.mismatch_in_text(SCREAMING_SNAKE_CASE	)
                    if mismatch_index == -1:
                           positions.append(SCREAMING_SNAKE_CASE	)
                    else:
                           _a    : int     					=self.match_in_pattern(self.text[mismatch_index]	)
                           _a    : List[str]     					=(
                               mismatch_index - match_index
                           )  # shifting index lgtm [py/multiple-definition]
             return positions

A__:    Any					    =       '''ABAABA'''
A__:    int					    =       '''AB'''
A__:    Optional[int]					    =       BoyerMooreSearch(text, pattern)
A__:    Optional[Any]					    =       bms.bad_character_heuristic()

if len(positions) == 0:
   print('''No match found''')
else:
   print('''Pattern found in following positions: ''')
   print(positions)


