






'''simple docstring'''


from __future__ import annotations







def 	__magic_name__( _A						,      _A = None						,      _A = None						,      _A = False						,      ):
						'''simple docstring'''





						UpperCamelCase__         = cipher_alphabet or [chr(_A					) 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)
												UpperCamelCase__         = {
												    """a""": 0.0_8_4_9_7,
												    """b""": 0.0_1_4_9_2,
												    """c""": 0.0_2_2_0_2,
												    """d""": 0.0_4_2_5_3,
												    """e""": 0.1_1_1_6_2,
												    """f""": 0.0_2_2_2_8,
												    """g""": 0.0_2_0_1_5,
												    """h""": 0.0_6_0_9_4,
												    """i""": 0.0_7_5_4_6,
												    """j""": 0.0_0_1_5_3,
												    """k""": 0.0_1_2_9_2,
												    """l""": 0.0_4_0_2_5,
												    """m""": 0.0_2_4_0_6,
												    """n""": 0.0_6_7_4_9,
												    """o""": 0.0_7_5_0_7,
												    """p""": 0.0_1_9_2_9,
												    """q""": 0.0_0_0_9_5,
												    """r""": 0.0_7_5_8_7,
												    """s""": 0.0_6_3_2_7,
												    """t""": 0.0_9_3_5_6,
												    """u""": 0.0_2_7_5_8,
												    """v""": 0.0_0_9_7_8,
												    """w""": 0.0_2_5_6_0,
												    """x""": 0.0_0_1_5_0,
												    """y""": 0.0_1_9_9_4,
												    """z""": 0.0_0_0_7_7,
												}
						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(_A					)					):
												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(
																								    _A					)
																								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(_A					)

																														# 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(_A					)

																														# 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(_A					) -> tuple[float, str]:
												return chi_squared_statistic_values[key]

						UpperCamelCase__         = min(
						    _A						,      key=_A						,      )

						# 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,
						)








'''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_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["pixel_values"]






			def __init__(   self							:						str ,      lowercase							:						bool = True ,      lowercase							:						int = 3_2 ,      lowercase							:						List[Any]=PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									UpperCamelCase__         = do_resize
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = size_divisor
									UpperCamelCase__         = resample
									super().__init__(**lowercase			)






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						int ,      lowercase							:						List[Any] ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						Any			)							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = get_image_size(lowercase			)
									# Rounds the height and width down to the closest multiple of size_divisor
									UpperCamelCase__         = height // size_divisor * size_divisor
									UpperCamelCase__         = width // size_divisor * size_divisor
									UpperCamelCase__         = resize(lowercase ,      (new_h, new_w) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
									return image






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						float ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						List[str]			)							->    np.ndarray:

									'''simple docstring'''







									return rescale(image=lowercase ,      scale=lowercase ,      data_format=lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[TensorType, str]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						str ,      )							->    BatchFeature:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = size_divisor if size_divisor is not None else self.size_divisor
									UpperCamelCase__         = 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"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															raise ValueError("""Invalid image(s)"""			)

									# All transformations expect numpy arrays.
									UpperCamelCase__         = [to_numpy_array(lowercase			) for img in images]

									if do_resize:
															UpperCamelCase__         = [self.resize(lowercase ,      size_divisor=lowercase ,      resample=lowercase			) for image in images]

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      scale=1 / 2_5_5			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)
