




from __future__ import annotations





class      snake_case	:


   '''simple docstring'''







   def __init__(       self  :						Union[str, Any]    ,     lowerCamelCase_  :						str    ,     lowerCamelCase_  :						str      )       ->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    ,       UpperCAmelCase__         = text, pattern
     UpperCAmelCase__    ,       UpperCAmelCase__         = len(lowerCamelCase_      ), len(lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						int    ,     lowerCamelCase_  :						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  :						Optional[Any]    ,     lowerCamelCase_  :						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  :						int      )       ->list[int]:
     '''simple docstring'''







     UpperCAmelCase__         = []
     for i in range(self.textLen - self.patLen + 1      ):
       UpperCAmelCase__         = self.mismatch_in_text(lowerCamelCase_      )
       if mismatch_index == -1:
         positions.append(lowerCamelCase_      )
       else:
         UpperCAmelCase__         = self.match_in_pattern(self.text[mismatch_index]      )
         UpperCAmelCase__         = (
             mismatch_index - match_index
         )  # shifting index lgtm [py/multiple-definition]
     return positions



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

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












import os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )