






'''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


UpperCAmelCase__						:str          =			logging.get_logger(__name__)







class 	SCREAMING_SNAKE_CASE	(      _UpperCAmelCase  ):
       snake_case__    :     str   		=			['''pixel_values''']




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


            """simple docstring"""







            super().__init__(**A_					)
            __lowerCamelCase						:       List[Any]				   =				size if size is not None else {"""shortest_edge""": 224}
            __lowerCamelCase						:       List[Any]				   =				get_size_dict(A_	,    default_to_square=A_					)
            __lowerCamelCase						:       Dict				   =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            __lowerCamelCase						:       str				   =				get_size_dict(A_	,    param_name="""crop_size"""					)

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




       def 					a_					(					self      : Any	,    A__      : np.ndarray	,    A__      : Dict[str, int]	,    A__      : PILImageResampling = PILImageResampling.BICUBIC	,    A__      : Optional[Union[str, ChannelDimension]] = None	,    **A__      : int	,    ):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				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:
                 __lowerCamelCase						:       int				   =				int((256 / 224) * size["""shortest_edge"""]					)
                 __lowerCamelCase						:       List[Any]				   =				get_resize_output_image_size(A_	,    size=A_	,    default_to_square=A_					)
                 __lowerCamelCase						:       Any				   =				{"""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 					a_					(					self      : Any	,    A__      : np.ndarray	,    A__      : Dict[str, int]	,    A__      : Optional[Union[str, ChannelDimension]] = None	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				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 					a_					(					self      : List[str]	,    A__      : np.ndarray	,    A__      : Union[int, float]	,    A__      : Optional[Union[str, ChannelDimension]] = None	,    **A__      : Optional[Any]	,    ):


            """simple docstring"""







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




       def 					a_					(					self      : Dict	,    A__      : np.ndarray	,    A__      : Union[float, List[float]]	,    A__      : Union[float, List[float]]	,    A__      : Optional[Union[str, ChannelDimension]] = None	,    **A__      : Optional[int]	,    ):


            """simple docstring"""







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




       def 					a_					(					self      : Union[str, Any]	,    A__      : ImageInput	,    A__      : Optional[bool] = None	,    A__      : Optional[Dict[str, int]] = None	,    A__      : PILImageResampling = None	,    A__      : Optional[bool] = None	,    A__      : Optional[Dict[str, int]] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[float] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[Union[float, Iterable[float]]] = None	,    A__      : Optional[Union[float, Iterable[float]]] = None	,    A__      : Optional[TensorType] = None	,    A__      : ChannelDimension = ChannelDimension.FIRST	,    **A__      : Any	,    ):


            """simple docstring"""







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

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

            __lowerCamelCase						:       int				   =				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.
            __lowerCamelCase						:       str				   =				[to_numpy_array(A_					) for image in images]

            if do_resize:
                 __lowerCamelCase						:       Union[str, Any]				   =				[self.resize(A_	,    A_	,    A_					) for image in images]

            if do_center_crop:
                 __lowerCamelCase						:       List[Any]				   =				[self.center_crop(A_	,    A_					) for image in images]

            if do_rescale:
                 __lowerCamelCase						:       List[str]				   =				[self.rescale(A_	,    A_					) for image in images]

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

            __lowerCamelCase						:       Any				   =				[to_channel_dimension_format(A_	,    A_					) for image in images]

            __lowerCamelCase						:       Any				   =				{"""pixel_values""": images}
            return BatchFeature(data=A_	,    tensor_type=A_					)











'''simple docstring'''

UpperCAmelCase__						:List[Any]          =			256
# Modulus to hash a string
UpperCAmelCase__						:str          =			1_000_003
def     __lowercase  (_lowercase,						_lowercase     )   ->     bool:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				len(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				len(_lowercase     )
     if p_len > t_len:
          return False

     __lowerCamelCase						:       Optional[Any]				   =				0
     __lowerCamelCase						:       Tuple				   =				0
     __lowerCamelCase						:       Dict				   =				1

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


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				"""abc1abc12"""
     __lowerCamelCase						:       Optional[Any]				   =				"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
     __lowerCamelCase						:       List[Any]				   =				"""alskfjaldsk23adsfabcabc"""
     assert rabin_karp(_lowercase,						_lowercase     ) and not rabin_karp(_lowercase,						_lowercase     )

     # Test 2)
     __lowerCamelCase						:       Optional[int]				   =				"""ABABX"""
     __lowerCamelCase						:       Dict				   =				"""ABABZABABYABABX"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 3)
     __lowerCamelCase						:       Any				   =				"""AAAB"""
     __lowerCamelCase						:       int				   =				"""ABAAAAAB"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 4)
     __lowerCamelCase						:       Any				   =				"""abcdabcy"""
     __lowerCamelCase						:       Dict				   =				"""abcxabcdabxabcdabcdabcy"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 5)
     __lowerCamelCase						:       str				   =				"""Lü"""
     __lowerCamelCase						:       str				   =				"""Lüsai"""
     assert rabin_karp(_lowercase,						_lowercase     )
     __lowerCamelCase						:       Tuple				   =				"""Lue"""
     assert not rabin_karp(_lowercase,						_lowercase     )
     print("""Success."""     )


if __name__ == "__main__":
   test_rabin_karp()



