






from __future__ import annotations


class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[Any]:
     UpperCamelCase_		,   UpperCamelCase_						=  text, pattern
     UpperCamelCase_		,   UpperCamelCase_						=  len(_UpperCAmelCase			), len(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:

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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:

     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			)      ->			list[int]:
     # searches pattern in text and returns index positions
     UpperCamelCase_						=  []
     for i in range(self.textLen - self.patLen + 1			):
        UpperCamelCase_						=  self.mismatch_in_text(_UpperCAmelCase			)
        if mismatch_index == -1:
           positions.append(_UpperCAmelCase			)
        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[str]													=   """ABAABA"""
snake_case__   :						Tuple													=   """AB"""
snake_case__   :						Tuple													=   BoyerMooreSearch(text, pattern)
snake_case__   :						Union[str, Any]													=   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_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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




