




from __future__ import annotations


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->list[int]:





           '''simple docstring'''

           A__							=   []
           for i in range(self.textLen - self.patLen + 1):
                  A__							=   self.mismatch_in_text(UpperCAmelCase__)
                  if mismatch_index == -1:
                         positions.append(UpperCAmelCase__)
                  else:
                         A__							=   self.match_in_pattern(self.text[mismatch_index])
                         A__							=   (
                             mismatch_index - match_index
                         )  # shifting index lgtm [py/multiple-definition]
           return positions
_lowerCamelCase     :		Any								=       """ABAABA"""
_lowerCamelCase     :		Any								=       """AB"""
_lowerCamelCase     :		Tuple								=       BoyerMooreSearch(text, pattern)
_lowerCamelCase     :		int								=       bms.bad_character_heuristic()

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








from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


