



from __future__ import annotations



class 	lowerCamelCase      :







     '''simple docstring'''







     def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
       UpperCAmelCase_					,						UpperCAmelCase_						=				text, pattern
       UpperCAmelCase_					,						UpperCAmelCase_						=				len(lowerCAmelCase     ), len(lowerCAmelCase     )



     def    A__		(			self    ,	lowerCAmelCase     ):

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



     def    A__		(			self    ,	lowerCAmelCase     ):

       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    A__		(			self     ):
       # searches pattern in text and returns index positions
       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





SCREAMING_SNAKE_CASE     				=						"ABAABA"
SCREAMING_SNAKE_CASE     				=						"AB"
SCREAMING_SNAKE_CASE     				=						BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE     				=						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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

