"""simple docstring"""






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


lowerCAmelCase__						:						Dict		  =			logging.get_logger(__name__)


if is_vision_available():
						import PIL
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["pixel_values"]







					def __init__(       self     :			int					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Union[int, float] = 1 / 255					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			bool = True					,**lowerCamelCase__     :			Any					,):
									super().__init__(**lowerCamelCase__    )
									UpperCAmelCase__    =							size if size is not None else {'shortest_edge': 224}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					,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 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									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(lowerCamelCase__					,size=size['shortest_edge']					,default_to_square=lowerCamelCase__    )
									return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Optional[int]					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									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(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Union[int, float]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			List[Any]					,):
									return rescale(lowerCamelCase__					,scale=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Union[float, List[float]]					,lowerCamelCase__     :			Union[float, List[float]]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Optional[Any]					,):
									return normalize(lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			ImageInput					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			Optional[ChannelDimension] = ChannelDimension.FIRST					,**lowerCamelCase__     :			Optional[int]					,):
									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(lowerCamelCase__					,param_name='size'					,default_to_square=lowerCamelCase__    )
									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(lowerCamelCase__					,param_name='crop_size'					,default_to_square=lowerCamelCase__    )
									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(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:
													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(lowerCamelCase__    ) for image in images]

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

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

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

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

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

									UpperCAmelCase__    =							[to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__    ) for image in images]

									UpperCAmelCase__    =							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__    )




"""simple docstring"""






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							""
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42

				for keychar, cipherchar in zip(cycle(lowerCamelCase )      ,    lowerCamelCase ):
								UpperCAmelCase__    =							cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(lowerCamelCase )

				return decoded



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for key in product(lowerCamelCase      ,    repeat=3 ):
								UpperCAmelCase__    =							try_key(lowerCamelCase      ,    lowerCamelCase )
								if encoded is not None:
												possibles.append(lowerCamelCase )
				return possibles



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							Path(lowerCamelCase ).parent.joinpath(lowerCamelCase ).read_text(encoding='utf-8' )

				UpperCAmelCase__    =							[int(lowerCamelCase ) for number in data.strip().split(',' )]

				UpperCAmelCase__    =							filter_valid_chars(lowerCamelCase )
				for common_word in COMMON_WORDS:
								UpperCAmelCase__    =							filter_common_word(lowerCamelCase      ,    lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												break

				UpperCAmelCase__    =							possibles[0]
				return sum(ord(lowerCamelCase ) for char in decoded_text )


if __name__ == "__main__":
						print(F"""{solution() = }""")



