




from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					list[str] | None = None  ,			A__       :					dict[str, float] | None = None  ,			A__       :					bool = False  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						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)
              __lowerCamelCase   =						{
                  """a""": 0.08_497,
                  """b""": 0.01_492,
                  """c""": 0.02_202,
                  """d""": 0.04_253,
                  """e""": 0.11_162,
                  """f""": 0.02_228,
                  """g""": 0.02_015,
                  """h""": 0.06_094,
                  """i""": 0.07_546,
                  """j""": 0.00_153,
                  """k""": 0.01_292,
                  """l""": 0.04_025,
                  """m""": 0.02_406,
                  """n""": 0.06_749,
                  """o""": 0.07_507,
                  """p""": 0.01_929,
                  """q""": 0.00_095,
                  """r""": 0.07_587,
                  """s""": 0.06_327,
                  """t""": 0.09_356,
                  """u""": 0.02_758,
                  """v""": 0.00_978,
                  """w""": 0.02_560,
                  """x""": 0.00_150,
                  """y""": 0.01_994,
                  """z""": 0.00_077,
              }
       else:
              # Custom frequencies dictionary
              __lowerCamelCase   =						frequencies_dict

       if not case_sensitive:
              __lowerCamelCase   =						ciphertext.lower()

       # Chi squared statistic values
       __lowerCamelCase   =						{}

       # cycle through all of the shifts
       for shift in range(len(A__	)	):
              __lowerCamelCase   =						""""""

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            __lowerCamelCase   =						(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

              __lowerCamelCase   =						0.0

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

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

                                   # Complete the chi squared statistic formula
                                   __lowerCamelCase   =						((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
                                   __lowerCamelCase   =						decrypted_with_shift.count(A__	)

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

                                   # Complete the chi squared statistic formula
                                   __lowerCamelCase   =						((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
              __lowerCamelCase   =						(
                  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__       :					int	) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       __lowerCamelCase   =						min(
           A__  ,			key=A__  ,			)

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           __lowerCamelCase
       ),   (
           __lowerCamelCase
       ),   
       )   =						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 Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							lowerCamelCase			      =					do_rescale
							lowerCamelCase			      =					rescale_factor
							lowerCamelCase			      =					do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase			      =					image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					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()}' )
							lowerCamelCase			      =					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    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					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` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       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 = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase			      =					rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase			      =					do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else self.image_mean
							lowerCamelCase			      =					image_std if image_std is not None else self.image_std

							lowerCamelCase			      =					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			      =					[to_numpy_array(_a ) for image in images]

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

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

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

							if do_normalize:
										lowerCamelCase			      =					[self.normalize(image=_a		, mean=_a		, std=_a ) for image in images]

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(_a ) != len(_a ):
													raise ValueError(
													    """Make sure that you pass in as many target sizes as the batch dimension of the logits""" )

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

										for idx in range(len(_a ) ):
													lowerCamelCase			      =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0 )		, size=target_sizes[idx]		, mode="""bilinear"""		, align_corners=_a )
													lowerCamelCase			      =					resized_logits[0].argmax(dim=0 )
													semantic_segmentation.append(_a )
							else:
										lowerCamelCase			      =					logits.argmax(dim=1 )
										lowerCamelCase			      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation



