"""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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCAmelCase__						:						Any		  =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["pixel_values"]







					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Union[int, float] = 1 / 255					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = True					,**lowerCamelCase__     :			str					,):
									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': 256, 'width': 256}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,param_name='crop_size'    )

									UpperCAmelCase__    =							do_resize
									UpperCAmelCase__    =							size
									UpperCAmelCase__    =							resample
									UpperCAmelCase__    =							do_rescale
									UpperCAmelCase__    =							rescale_factor
									UpperCAmelCase__    =							do_center_crop
									UpperCAmelCase__    =							crop_size
									UpperCAmelCase__    =							do_flip_channel_order







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			PILImageResampling = PIL.Image.BILINEAR					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									if "shortest_edge" not in size:
													raise ValueError(f'''The `size` dictionary 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     :			List[Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			List[Any]					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''    )
									return center_crop(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[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     :			Union[str, Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None    ):
									return flip_channel_order(lowerCamelCase__					,data_format=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			ImageInput					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__    =							resample if resample is not None else self.resample
									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_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__    =							(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									UpperCAmelCase__    =							size if size is not None else self.size
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,param_name='crop_size'    )

									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_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

									if do_center_crop and crop_size is None:
													raise ValueError('Crop size must be specified if do_center_crop is True.'    )

									# 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]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
													UpperCAmelCase__    =							[self.flip_channel_order(image=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__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Tuple] = None    ):
									UpperCAmelCase__    =							outputs.logits

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

													if is_torch_tensor(lowerCamelCase__    ):
																	UpperCAmelCase__    =							target_sizes.numpy()

													UpperCAmelCase__    =							[]

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

									return semantic_segmentation




"""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() = }""")



