





'''simple docstring'''
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


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

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=["pixel_values"]






 def __init__(       self       :							Optional[Any]							,	__A       :							bool = True							,	__A       :							Union[int, float] = 1 / 2_5_5							,	__A       :							bool = True							,	__A       :							int = 8							,	**__A       :							Dict							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  do_rescale
        __UpperCamelCase       =  rescale_factor
        __UpperCamelCase       =  do_pad
        __UpperCamelCase       =  pad_size






 def        _lowerCamelCase					(       self       :							str							,	__A       :							np.ndarray							,	__A       :							float							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							List[str]	):
        return rescale(__A							,	scale=__A							,	data_format=__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							np.ndarray							,	__A       :							int							,	__A       :							Optional[Union[str, ChannelDimension]] = None	):
        __UpperCamelCase       ,						__UpperCamelCase       =  get_image_size(__A	)
        __UpperCamelCase       =  (old_height // size + 1) * size - old_height
        __UpperCamelCase       =  (old_width // size + 1) * size - old_width

        return pad(__A							,	((0, pad_height), (0, pad_width))							,	mode='symmetric'							,	data_format=__A	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							ImageInput							,	__A       :							Optional[bool] = None							,	__A       :							Optional[float] = None							,	__A       :							Optional[bool] = None							,	__A       :							Optional[int] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**__A       :							str							,	):
        __UpperCamelCase       =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase       =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase       =  do_pad if do_pad is not None else self.do_pad
        __UpperCamelCase       =  pad_size if pad_size is not None else self.pad_size

        __UpperCamelCase       =  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_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

        # All transformations expect numpy arrays.
        __UpperCamelCase       =  [to_numpy_array(__A	) for image in images]

        if do_rescale:
               __UpperCamelCase       =  [self.rescale(image=__A							,	scale=__A	) for image in images]

        if do_pad:
               __UpperCamelCase       =  [self.pad(__A							,	size=__A	) for image in images]

        __UpperCamelCase       =  [to_channel_dimension_format(__A							,	__A	) for image in images]

        __UpperCamelCase       =  {'pixel_values': images}
        return BatchFeature(data=__A							,	tensor_type=__A	)







'''simple docstring'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (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
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
