






"""simple docstring"""



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				__a		):
							snake_case_     =							['pixel_values']


							def __init__(			self      :						str			,    snake_case      :						bool = True			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						PILImageResampling = PILImageResampling.BICUBIC			,    snake_case      :						bool = True			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						bool = True			,    snake_case      :						Union[int, float] = 1 / 255			,    snake_case      :						bool = True			,    snake_case      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,    snake_case      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													super().__init__(**a_							)
													A__						:  Union[str, Any]		  =  size if size is not None else {"""shortest_edge""": 224}
													A__						:  str		  =  get_size_dict(a_			,    default_to_square=a_							)
													A__						:  Optional[int]		  =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
													A__						:  List[str]		  =  get_size_dict(a_			,    param_name="""crop_size"""							)

													A__						:  int		  =  do_resize
													A__						:  str		  =  size
													A__						:  List[Any]		  =  resample
													A__						:  List[Any]		  =  do_center_crop
													A__						:  Any		  =  crop_size
													A__						:  Union[str, Any]		  =  do_rescale
													A__						:  str		  =  rescale_factor
													A__						:  Union[str, Any]		  =  do_normalize
													A__						:  Union[str, Any]		  =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													A__						:  int		  =  image_std if image_std is not None else IMAGENET_DEFAULT_STD


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						np.ndarray			,    snake_case      :						Dict[str, int]			,    snake_case      :						PILImageResampling = PILImageResampling.BICUBIC			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						List[Any]			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  get_size_dict(a_			,    default_to_square=a_							)
													# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
													if "shortest_edge" in size:
																			A__						:  List[str]		  =  int((256 / 224) * size["""shortest_edge"""]							)
																			A__						:  Tuple		  =  get_resize_output_image_size(a_			,    size=a_			,    default_to_square=a_							)
																			A__						:  Dict		  =  {"""height""": output_size[0], """width""": output_size[1]}
													if "height" not in size_dict or "width" not in size_dict:
																			raise ValueError(
																			    F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'							)
													return resize(
													    a_			,    size=(size_dict["""height"""], size_dict["""width"""])			,    resample=a_			,    data_format=a_			,    **a_							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						np.ndarray			,    snake_case      :						Dict[str, int]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Dict		  =  get_size_dict(a_							)
													if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'							)
													return center_crop(a_			,    size=(size["""height"""], size["""width"""])			,    data_format=a_			,    **a_							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						np.ndarray			,    snake_case      :						Union[int, float]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													return rescale(a_			,    scale=a_			,    data_format=a_			,    **a_							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						np.ndarray			,    snake_case      :						Union[float, List[float]]			,    snake_case      :						Union[float, List[float]]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						Tuple			,    ):
													'''simple docstring'''





													return normalize(a_			,    mean=a_			,    std=a_			,    data_format=a_			,    **a_							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						ImageInput			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Dict[str, int]] = None			,    snake_case      :						PILImageResampling = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Dict[str, int]] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[float] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Union[float, Iterable[float]]] = None			,    snake_case      :						Optional[Union[float, Iterable[float]]] = None			,    snake_case      :						Optional[TensorType] = None			,    snake_case      :						ChannelDimension = ChannelDimension.FIRST			,    **snake_case      :						List[str]			,    ):
													'''simple docstring'''





													A__						:  Any		  =  do_resize if do_resize is not None else self.do_resize
													A__						:  Union[str, Any]		  =  resample if resample is not None else self.resample
													A__						:  Tuple		  =  do_center_crop if do_center_crop is not None else self.do_center_crop
													A__						:  Dict		  =  do_rescale if do_rescale is not None else self.do_rescale
													A__						:  Tuple		  =  rescale_factor if rescale_factor is not None else self.rescale_factor
													A__						:  List[Any]		  =  do_normalize if do_normalize is not None else self.do_normalize
													A__						:  Tuple		  =  image_mean if image_mean is not None else self.image_mean
													A__						:  int		  =  image_std if image_std is not None else self.image_std

													A__						:  Union[str, Any]		  =  size if size is not None else self.size
													A__						:  Tuple		  =  get_size_dict(a_			,    default_to_square=a_							)
													A__						:  str		  =  crop_size if crop_size is not None else self.crop_size
													A__						:  int		  =  get_size_dict(a_			,    param_name="""crop_size"""							)

													A__						:  Optional[Any]		  =  make_list_of_images(a_							)

													if not valid_images(a_							):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

													if do_resize and size is None:
																			raise ValueError("""Size must be specified if do_resize is True."""							)

													if do_center_crop and crop_size is None:
																			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

													if do_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

													# All transformations expect numpy arrays.
													A__						:  Any		  =  [to_numpy_array(a_							) for image in images]

													if do_resize:
																			A__						:  List[Any]		  =  [self.resize(a_			,    a_			,    a_							) for image in images]

													if do_center_crop:
																			A__						:  str		  =  [self.center_crop(a_			,    a_							) for image in images]

													if do_rescale:
																			A__						:  Optional[Any]		  =  [self.rescale(a_			,    a_							) for image in images]

													if do_normalize:
																			A__						:  Optional[int]		  =  [self.normalize(a_			,    a_			,    a_							) for image in images]

													A__						:  Tuple		  =  [to_channel_dimension_format(a_			,    a_							) for image in images]

													A__						:  Tuple		  =  {"""pixel_values""": images}
													return BatchFeature(data=a_			,    tensor_type=a_							)














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Any			,    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 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




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

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






