


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__:    Dict					    =       {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Union[str, Any]					    =       [
       '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''XLMForMultipleChoice''',
       '''XLMForQuestionAnswering''',
       '''XLMForQuestionAnsweringSimple''',
       '''XLMForSequenceClassification''',
       '''XLMForTokenClassification''',
       '''XLMModel''',
       '''XLMPreTrainedModel''',
       '''XLMWithLMHeadModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Any					    =       [
       '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFXLMForMultipleChoice''',
       '''TFXLMForQuestionAnsweringSimple''',
       '''TFXLMForSequenceClassification''',
       '''TFXLMForTokenClassification''',
       '''TFXLMMainLayer''',
       '''TFXLMModel''',
       '''TFXLMPreTrainedModel''',
       '''TFXLMWithLMHeadModel''',
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   A__:    List[Any]					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''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)


