




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A        =    logging.get_logger(__name__)







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''pixel_values''']


			def __init__(      self			,						lowerCamelCase__ = True			,						lowerCamelCase__ = 32			,						lowerCamelCase__=PILImageResampling.BILINEAR			,						lowerCamelCase__ = True			,						**lowerCamelCase__			,						)    ->      None:



							'''simple docstring'''
							__lowerCamelCase		       =    do_resize
							__lowerCamelCase		       =    do_rescale
							__lowerCamelCase		       =    size_divisor
							__lowerCamelCase		       =    resample
							super().__init__(**lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						**lowerCamelCase__							)    ->      np.ndarray:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    get_image_size(lowerCamelCase__							)
							# Rounds the height and width down to the closest multiple of size_divisor
							__lowerCamelCase		       =    height // size_divisor * size_divisor
							__lowerCamelCase		       =    width // size_divisor * size_divisor
							__lowerCamelCase		       =    resize(lowerCamelCase__			,						(new_h, new_w)			,						resample=lowerCamelCase__			,						data_format=lowerCamelCase__			,						**lowerCamelCase__							)
							return image


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						**lowerCamelCase__							)    ->      np.ndarray:



							'''simple docstring'''
							return rescale(image=lowerCamelCase__			,						scale=lowerCamelCase__			,						data_format=lowerCamelCase__			,						**lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__=None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = ChannelDimension.FIRST			,						**lowerCamelCase__			,						)    ->      BatchFeature:



							'''simple docstring'''
							__lowerCamelCase		       =    do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase		       =    do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase		       =    size_divisor if size_divisor is not None else self.size_divisor
							__lowerCamelCase		       =    resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

							__lowerCamelCase		       =    make_list_of_images(lowerCamelCase__							)

							if not valid_images(lowerCamelCase__							):
											raise ValueError('Invalid image(s)'							)

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

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

							if do_rescale:
											__lowerCamelCase		       =    [self.rescale(lowerCamelCase__			,						scale=1 / 255							) for image in images]

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

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








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__A        =    {
    "E": 1_2.7_0,
    "T": 9.0_6,
    "A": 8.1_7,
    "O": 7.5_1,
    "I": 6.9_7,
    "N": 6.7_5,
    "S": 6.3_3,
    "H": 6.0_9,
    "R": 5.9_9,
    "D": 4.2_5,
    "L": 4.0_3,
    "C": 2.7_8,
    "U": 2.7_6,
    "M": 2.4_1,
    "W": 2.3_6,
    "F": 2.2_3,
    "G": 2.0_2,
    "Y": 1.9_7,
    "P": 1.9_3,
    "B": 1.2_9,
    "V": 0.9_8,
    "K": 0.7_7,
    "J": 0.1_5,
    "X": 0.1_5,
    "Q": 0.1_0,
    "Z": 0.0_7,
}
__A        =    "ETAOINSHRDLCUMWFGYPBVKJXQZ"
__A        =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       dict[str, int]:





				"""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_		(       UpperCamelCase__				:      tuple      )     ->       str:





				"""simple docstring"""


				return x[0]







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


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

				__lowerCamelCase		       =    {}

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

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

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

				return "".join(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    get_frequency_order(UpperCamelCase__      )
				__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()


