


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


snake_case				:    Dict       				=     logging.get_logger(__name__)







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['pixel_values']




 def __init__(		self					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  _lowerCamelCase = PIL.Image.BICUBIC					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  _lowerCamelCase = 1 / 255					,  _lowerCamelCase = True					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(**_lowerCamelCase    )
       a						:Optional[Any] 							=       size if size is not None else {'''height''': 256, '''width''': 256}
       a						:Optional[Any] 							=       get_size_dict(_lowerCamelCase    )
       a						:Union[str, Any] 							=       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a						:Union[str, Any] 							=       get_size_dict(_lowerCamelCase					,  param_name='''crop_size'''    )

       a						:List[Any] 							=       do_resize
       a						:Union[str, Any] 							=       size
       a						:Optional[int] 							=       resample
       a						:Dict 							=       do_center_crop
       a						:List[Any] 							=       crop_size
       a						:Optional[int] 							=       do_rescale
       a						:List[Any] 							=       rescale_factor
       a						:List[Any] 							=       do_normalize
       a						:int 							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       a						:Any 							=       image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = PIL.Image.BICUBIC					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       a						:Optional[Any] 							=       get_size_dict(_lowerCamelCase    )
       if "height" not in size or "width" not in size:
             raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
       return resize(
           _lowerCamelCase					,  size=(size['''height'''], size['''width'''])					,  resample=_lowerCamelCase					,  data_format=_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       a						:Optional[Any] 							=       get_size_dict(_lowerCamelCase    )
       if "height" not in size or "width" not in size:
             raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
       return center_crop(_lowerCamelCase					,  size=(size['''height'''], size['''width'''])					,  data_format=_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       return rescale(_lowerCamelCase					,  scale=_lowerCamelCase					,  data_format=_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       return normalize(_lowerCamelCase					,  mean=_lowerCamelCase					,  std=_lowerCamelCase					,  data_format=_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase=None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = ChannelDimension.FIRST					,  **_lowerCamelCase					,  ):
       a						:List[str] 							=       do_resize if do_resize is not None else self.do_resize
       a						:Any 							=       resample if resample is not None else self.resample
       a						:List[Any] 							=       do_center_crop if do_center_crop is not None else self.do_center_crop
       a						:int 							=       do_rescale if do_rescale is not None else self.do_rescale
       a						:Optional[int] 							=       rescale_factor if rescale_factor is not None else self.rescale_factor
       a						:Union[str, Any] 							=       do_normalize if do_normalize is not None else self.do_normalize
       a						:Optional[int] 							=       image_mean if image_mean is not None else self.image_mean
       a						:Union[str, Any] 							=       image_std if image_std is not None else self.image_std

       a						:str 							=       size if size is not None else self.size
       a						:str 							=       get_size_dict(_lowerCamelCase    )
       a						:Optional[Any] 							=       crop_size if crop_size is not None else self.crop_size
       a						:Union[str, Any] 							=       get_size_dict(_lowerCamelCase					,  param_name='''crop_size'''    )

       a						:Union[str, Any] 							=       make_list_of_images(_lowerCamelCase    )

       if not valid_images(_lowerCamelCase    ):
             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 or resample is None:
             raise ValueError('''Size and resample 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						:str 							=       [to_numpy_array(_lowerCamelCase    ) for image in images]

       if do_resize:
             a						:Optional[int] 							=       [self.resize(image=_lowerCamelCase					,  size=_lowerCamelCase					,  resample=_lowerCamelCase    ) for image in images]

       if do_center_crop:
             a						:int 							=       [self.center_crop(image=_lowerCamelCase					,  size=_lowerCamelCase    ) for image in images]

       if do_rescale:
             a						:Any 							=       [self.rescale(image=_lowerCamelCase					,  scale=_lowerCamelCase    ) for image in images]

       if do_normalize:
             a						:Dict 							=       [self.normalize(image=_lowerCamelCase					,  mean=_lowerCamelCase					,  std=_lowerCamelCase    ) for image in images]

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

       a						:str 							=       {'''pixel_values''': images}
       return BatchFeature(data=_lowerCamelCase					,  tensor_type=_lowerCamelCase    )









from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		list[str] | None = None ,  UpperCAmelCase_						:		dict[str, float] | None = None ,  UpperCAmelCase_						:		bool = False ,  ):




      """simple docstring"""





      a						:str 							=       cipher_alphabet or [chr(UpperCAmelCase_						) for i in range(97 ,  123						)]

      # 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)
            a						:List[Any] 							=       {
                '''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
            a						:Dict 							=       frequencies_dict

      if not case_sensitive:
            a						:Union[str, Any] 							=       ciphertext.lower()

      # Chi squared statistic values
      a						:dict[int, tuple[float, str]] 							=       {}

      # cycle through all of the shifts
      for shift in range(len(UpperCAmelCase_						)						):
            a						:int 							=       ''''''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        a						:Dict 							=       (alphabet_letters.index(letter.lower()						) - shift) % len(
                            UpperCAmelCase_						)
                        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

            a						:List[Any] 							=       0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        a						:Optional[int] 							=       letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:List[Any] 							=       decrypted_with_shift.lower().count(UpperCAmelCase_						)

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

                              # Complete the chi squared statistic formula
                              a						:Any 							=       ((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
                              a						:int 							=       decrypted_with_shift.count(UpperCAmelCase_						)

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

                              # Complete the chi squared statistic formula
                              a						:Optional[Any] 							=       ((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
            a						:Optional[Any] 							=       (
                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(UpperCAmelCase_						:		int						) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      a						:int 							=       min(
          UpperCAmelCase_ ,  key=UpperCAmelCase_ ,  )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          a
      )							,				(
          a
      )							,				
      )						:Optional[int] 							=       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,
      )





