





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      if isinstance(SCREAMING_SNAKE_CASE							,						(list, tuple)			) and isinstance(videos[0]							,						(list, tuple)			) and is_valid_image(videos[0][0]			):
            return videos

      elif isinstance(SCREAMING_SNAKE_CASE							,						(list, tuple)			) and is_valid_image(videos[0]			):
            return [videos]

      elif is_valid_image(SCREAMING_SNAKE_CASE			):
            return [[videos]]

      raise ValueError(F'Could not make batched video from {videos}'			)






class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['pixel_values']





      def __init__(       self      ,						lowercase = True      ,						lowercase = None      ,						lowercase = PILImageResampling.BILINEAR      ,						lowercase = True      ,						lowercase = None      ,						lowercase = True      ,						lowercase = 1 / 255      ,						lowercase = True      ,						lowercase = True      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            super().__init__(**lowercase )
            lowerCAmelCase  						=       size if size is not None else {"""shortest_edge""": 256}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""crop_size""" )

            lowerCAmelCase  						=       do_resize
            lowerCAmelCase  						=       size
            lowerCAmelCase  						=       do_center_crop
            lowerCAmelCase  						=       crop_size
            lowerCAmelCase  						=       resample
            lowerCAmelCase  						=       do_rescale
            lowerCAmelCase  						=       rescale_factor
            lowerCAmelCase  						=       offset
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = PILImageResampling.BILINEAR      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            if "shortest_edge" in size:
                  lowerCAmelCase  						=       get_resize_output_image_size(lowercase      ,						size["""shortest_edge"""]      ,						default_to_square=lowercase )
            elif "height" in size and "width" in size:
                  lowerCAmelCase  						=       (size["""height"""], size["""width"""])
            else:
                  raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
            return resize(lowercase      ,						size=lowercase      ,						resample=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase )
            if "height" not in size or "width" not in size:
                  raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
            return center_crop(lowercase      ,						size=(size["""height"""], size["""width"""])      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = True      ,						lowercase = None      ,						**lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       image.astype(np.floataa )
            if offset:
                  lowerCAmelCase  						=       image - (scale / 2)
            return rescale(lowercase      ,						scale=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            return normalize(lowercase      ,						mean=lowercase      ,						std=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = ChannelDimension.FIRST      ,						)		->     np.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.""" )

            if offset and not do_rescale:
                  raise ValueError("""For offset, do_rescale must also be set to True.""" )

            # All transformations expect numpy arrays.
            lowerCAmelCase  						=       to_numpy_array(lowercase )

            if do_resize:
                  lowerCAmelCase  						=       self.resize(image=lowercase      ,						size=lowercase      ,						resample=lowercase )

            if do_center_crop:
                  lowerCAmelCase  						=       self.center_crop(lowercase      ,						size=lowercase )

            if do_rescale:
                  lowerCAmelCase  						=       self.rescale(image=lowercase      ,						scale=lowercase      ,						offset=lowercase )

            if do_normalize:
                  lowerCAmelCase  						=       self.normalize(image=lowercase      ,						mean=lowercase      ,						std=lowercase )

            lowerCAmelCase  						=       to_channel_dimension_format(lowercase      ,						lowercase )
            return image





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = ChannelDimension.FIRST      ,						**lowercase      ,						)		->     PIL.Image.Image:
            lowerCAmelCase  						=       do_resize if do_resize is not None else self.do_resize
            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  						=       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  						=       offset if offset is not None else self.offset
            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  						=       size if size is not None else self.size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""crop_size""" )

            if not valid_images(lowercase ):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray.""" )

            lowerCAmelCase  						=       make_batched(lowercase )

            lowerCAmelCase  						=       [
                [
                    self._preprocess_image(
                        image=lowercase      ,						do_resize=lowercase      ,						size=lowercase      ,						resample=lowercase      ,						do_center_crop=lowercase      ,						crop_size=lowercase      ,						do_rescale=lowercase      ,						rescale_factor=lowercase      ,						offset=lowercase      ,						do_normalize=lowercase      ,						image_mean=lowercase      ,						image_std=lowercase      ,						data_format=lowercase      ,						)
                    for img in video
                ]
                for video in videos
            ]

            lowerCAmelCase  						=       {"""pixel_values""": videos}
            return BatchFeature(data=lowercase      ,						tensor_type=lowercase )













from __future__ import annotations

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

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

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

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

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

   return decoded

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

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

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

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


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

