




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCAmelCase_									=	{
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
UpperCAmelCase_									=	'ETAOINSHRDLCUMWFGYPBVKJXQZ'
UpperCAmelCase_									=	'ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						{letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count




def 					lowerCamelCase__       (						A__       :					tuple	):





       '''simple docstring'''



       return x[0]




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						get_letter_count(A__	)
       __lowerCamelCase   =						{
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(A__	)

       __lowerCamelCase   =						{}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find  ,			reverse=A__	)
              __lowerCamelCase   =						"""""".join(freq_to_letter[freq]	)

       __lowerCamelCase   =						list(freq_to_letter_str.items()	)
       freq_pairs.sort(key=A__  ,			reverse=A__	)

       __lowerCamelCase   =						[freq_pair[1] for freq_pair in freq_pairs]

       return "".join(A__	)




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						get_frequency_order(A__	)
       __lowerCamelCase   =						0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __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_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

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

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

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

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

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

