
"""simple docstring"""


from __future__ import annotations







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Optional[int]	,A_  :   str	,A_  :   str   )  -> int:
							A ,   A     = text, pattern
							A ,   A     = len(A_   ), len(A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   int   )  -> 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     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> list[int]:
							# searches pattern in text and returns index positions
							A     = []
							for i in range(self.textLen - self.patLen + 1   ):
								A     = self.mismatch_in_text(A_   )
								if mismatch_index == -1:
									positions.append(A_   )
								else:
									A     = self.match_in_pattern(self.text[mismatch_index]   )
									A     = (
									    mismatch_index - match_index
									)  # shifting index lgtm [py/multiple-definition]
							return positions

_lowercase				 =					'''ABAABA'''
_lowercase				 =					'''AB'''
_lowercase				 =					BoyerMooreSearch(text, pattern)
_lowercase				 =					bms.bad_character_heuristic()

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






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


