



'''simple docstring'''






lowerCAmelCase: Dict							=						2_5_6
# Modulus to hash a string
lowerCAmelCase: Union[str, Any]							=						1_0_0_0_0_0_3
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Dict					       =					len(_UpperCAmelCase    )
  a						: Union[str, Any]					       =					len(_UpperCAmelCase    )
  if p_len > t_len:
    return False

  a						: Union[str, Any]					       =					0
  a						: Dict					       =					0
  a						: Any					       =					1

  # Calculating the hash of pattern and substring of text
  for i in range(_UpperCAmelCase    ):
    a						: Dict					       =					(ord(pattern[i]    ) + p_hash * alphabet_size) % modulus
    a						: Tuple					       =					(ord(text[i]    ) + text_hash * alphabet_size) % modulus
    if i == p_len - 1:
      continue
    a						: Tuple					       =					(modulus_power * alphabet_size) % modulus

  for i in range(0			,      t_len - p_len + 1    ):
    if text_hash == p_hash and text[i : i + p_len] == pattern:
      return True
    if i == t_len - p_len:
      continue
    # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
    a						: str					       =					(
        (text_hash - ord(text[i]    ) * modulus_power) * alphabet_size
        + ord(text[i + p_len]    )
    ) % modulus
  return False
def 				lowerCamelCase__	(						):
  a						: Any					       =					'abc1abc12'
  a						: int					       =					'alskfjaldsabc1abc1abc12k23adsfabcabc'
  a						: Optional[int]					       =					'alskfjaldsk23adsfabcabc'
  assert rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    ) and not rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )

  # Test 2)
  a						: str					       =					'ABABX'
  a						: Tuple					       =					'ABABZABABYABABX'
  assert rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )

  # Test 3)
  a						: int					       =					'AAAB'
  a						: Union[str, Any]					       =					'ABAAAAAB'
  assert rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )

  # Test 4)
  a						: Union[str, Any]					       =					'abcdabcy'
  a						: List[str]					       =					'abcxabcdabxabcdabcdabcy'
  assert rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )

  # Test 5)
  a						: Dict					       =					'Lü'
  a						: Dict					       =					'Lüsai'
  assert rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )
  a						: List[Any]					       =					'Lue'
  assert not rabin_karp(_UpperCAmelCase			,      _UpperCAmelCase    )
  print('Success.'    )


if __name__ == "__main__":
     test_rabin_karp()




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )