

"""simple docstring"""



from __future__ import annotations



class 					a						:



				"""simple docstring"""



				def __init__(     self: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: str       ):






									"""simple docstring"""


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

				def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






									"""simple docstring"""


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

				def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: 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: List[Any]       ):






									"""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

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

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









"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_					:	List[str]							  =						{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 SCREAMING_SNAKE_CASE_					:	List[str]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure)






