




from __future__ import annotations



class    lowercase_		:



						def __init__(			self	:   List[str]					,							snake_case__	:   str					,							snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							text, pattern
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							len(snake_case__  ), len(snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   str  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Dict					,							snake_case__	:   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 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[]
								for i in range(self.textLen - self.patLen + 1  ):
										SCREAMING_SNAKE_CASE_					      =							self.mismatch_in_text(snake_case__  )
										if mismatch_index == -1:
												positions.append(snake_case__  )
										else:
												SCREAMING_SNAKE_CASE_					      =							self.match_in_pattern(self.text[mismatch_index]  )
												SCREAMING_SNAKE_CASE_					      =							(
												    mismatch_index - match_index
												)  # shifting index lgtm [py/multiple-definition]
								return positions

SCREAMING_SNAKE_CASE:     str									= '''ABAABA'''
SCREAMING_SNAKE_CASE:     Tuple									= '''AB'''
SCREAMING_SNAKE_CASE:     List[Any]									= BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE:     Tuple									= bms.bad_character_heuristic()

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





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE:     Optional[int]									= logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Any					,							**snake_case__	:   str  ):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								requires_backends(self					,							'vision'  )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )



						def __call__(			self	:   str					,							snake_case__	:   Union[str, List[str], "Image", List["Image"]]					,							**snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								return super().__call__(snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   str					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if "candidate_labels" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['hypothesis_template']

								return preprocess_params, {}, {}



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]="This is a photo of {}."  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor(images=[image]					,							return_tensors=self.framework  )
								SCREAMING_SNAKE_CASE_					      =							candidate_labels
								SCREAMING_SNAKE_CASE_					      =							[hypothesis_template.format(snake_case__  ) for x in candidate_labels]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							return_tensors=self.framework					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[text_inputs]
								return inputs



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('text_inputs'  )
								if isinstance(text_inputs[0]					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0]
								else:
										# Batching case.
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0][0]

								SCREAMING_SNAKE_CASE_					      =							self.model(**snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_outputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_outputs['logits'][0]
								if self.framework == "pt":
										SCREAMING_SNAKE_CASE_					      =							logits.softmax(dim=-1  ).squeeze(-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.tolist()
										if not isinstance(snake_case__					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							[scores]
								elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_					      =							stable_softmax(snake_case__					,							axis=-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.numpy().tolist()
								else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(snake_case__					,							snake_case__  )					,							key=lambda snake_case__  : -x[0]  )
								]
								return result