



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_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

