


from __future__ import annotations

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : list[str] | None = None      ,					SCREAMING_SNAKE_CASE__      : dict[str, float] | None = None      ,					SCREAMING_SNAKE_CASE__      : bool = False      ,					):
     __UpperCamelCase   							=cipher_alphabet or [chr(SCREAMING_SNAKE_CASE__    ) for i in range(97      ,					1_23    )]

     # If the argument is None or the user provided an empty dictionary
     if not frequencies_dict:
          # Frequencies of letters in the english language (how much they show up)
          __UpperCamelCase   							={
              'a': 0.08497,
              'b': 0.01492,
              'c': 0.02202,
              'd': 0.04253,
              'e': 0.11162,
              'f': 0.02228,
              'g': 0.02015,
              'h': 0.06094,
              'i': 0.07546,
              'j': 0.00153,
              'k': 0.01292,
              'l': 0.04025,
              'm': 0.02406,
              'n': 0.06749,
              'o': 0.07507,
              'p': 0.01929,
              'q': 0.00095,
              'r': 0.07587,
              's': 0.06327,
              't': 0.09356,
              'u': 0.02758,
              'v': 0.00978,
              'w': 0.02560,
              'x': 0.00150,
              'y': 0.01994,
              'z': 0.00077,
          }
     else:
          # Custom frequencies dictionary
          __UpperCamelCase   							=frequencies_dict

     if not case_sensitive:
          __UpperCamelCase   							=ciphertext.lower()

     # Chi squared statistic values
     __UpperCamelCase   							={}

     # cycle through all of the shifts
     for shift in range(len(SCREAMING_SNAKE_CASE__    )    ):
          __UpperCamelCase   							=''

          # decrypt the message with the shift
          for letter in ciphertext:
               try:
                    # Try to index the letter in the alphabet
                    __UpperCamelCase   							=(alphabet_letters.index(letter.lower()    ) - shift) % len(
                        SCREAMING_SNAKE_CASE__    )
                    decrypted_with_shift += (
                        alphabet_letters[new_key].upper()
                        if case_sensitive and letter.isupper()
                        else alphabet_letters[new_key]
                    )
               except ValueError:
                    # Append the character if it isn't in the alphabet
                    decrypted_with_shift += letter

          __UpperCamelCase   							=0.0

          # Loop through each letter in the decoded message with the shift
          for letter in decrypted_with_shift:
               if case_sensitive:
                    __UpperCamelCase   							=letter.lower()
                    if letter in frequencies:
                         # Get the amount of times the letter occurs in the message
                         __UpperCamelCase   							=decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE__    )

                         # Get the excepcted amount of times the letter should appear based
                         # on letter frequencies
                         __UpperCamelCase   							=frequencies[letter] * occurrences

                         # Complete the chi squared statistic formula
                         __UpperCamelCase   							=((occurrences - expected) ** 2) / expected

                         # Add the margin of error to the total chi squared statistic
                         chi_squared_statistic += chi_letter_value
               else:
                    if letter.lower() in frequencies:
                         # Get the amount of times the letter occurs in the message
                         __UpperCamelCase   							=decrypted_with_shift.count(SCREAMING_SNAKE_CASE__    )

                         # Get the excepcted amount of times the letter should appear based
                         # on letter frequencies
                         __UpperCamelCase   							=frequencies[letter] * occurrences

                         # Complete the chi squared statistic formula
                         __UpperCamelCase   							=((occurrences - expected) ** 2) / expected

                         # Add the margin of error to the total chi squared statistic
                         chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
          __UpperCamelCase   							=(
              chi_squared_statistic,
              decrypted_with_shift,
          )

     # Get the most likely cipher by finding the cipher with the smallest chi squared
     # statistic
     def chi_squared_statistic_values_sorting_key(SCREAMING_SNAKE_CASE__      : int    ) -> tuple[float, str]:
          return chi_squared_statistic_values[key]

     __UpperCamelCase   							=min(
         SCREAMING_SNAKE_CASE__      ,					key=SCREAMING_SNAKE_CASE__      ,					)

     # Get all the data from the most likely cipher (key, decoded message)
     (
         (
         __UpperCamelCase				
     )							,				(
         __UpperCamelCase				
     )							,								
     )   							=chi_squared_statistic_values[most_likely_cipher]

     # Return the data on the most likely shift
     return (
         most_likely_cipher,
         most_likely_cipher_chi_squared_value,
         decoded_most_likely_cipher,
     )









from typing import Dict, List, Optional, Union

import numpy as np

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


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(A_ ,       size=size['shortest_edge'] ,       default_to_square=A_   )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __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_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __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_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.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

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

         if do_resize:
              __UpperCamelCase   							=[self.resize(image=A_ ,       size=A_ ,       resample=A_   ) for image in images]

         if do_center_crop:
              __UpperCamelCase   							=[self.center_crop(image=A_ ,       size=A_   ) for image in images]

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

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=A_ ,       mean=A_ ,       std=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_   )





