
"""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


_a														=		logging.get_logger(__name__)


if is_vision_available():
				import PIL






class     _UpperCAmelCase(    __lowercase	):
		lowercase__						=      ["pixel_values"]



		def __init__(					self				,		__a = True				,		__a = None				,		__a = PILImageResampling.BICUBIC				,		__a = True				,		__a = None				,		__a = True				,		__a = 1 / 2_55				,		__a = True				,		__a = None				,		__a = None				,		__a = True				,		**__a				,		)       ->       List[str]:






							'''simple docstring'''
							super().__init__(**snake_case_)
							_UpperCamelCase							 =  size if size is not None else {'''shortest_edge''': 2_24}
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_)
							_UpperCamelCase							 =  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_				,		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 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = PILImageResampling.BICUBIC				,		__a = None				,		**__a				,		)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  get_size_dict(snake_case_				,		default_to_square=snake_case_)
							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(snake_case_				,		size=size['''shortest_edge''']				,		default_to_square=snake_case_)
							return resize(snake_case_				,		size=snake_case_				,		resample=snake_case_				,		data_format=snake_case_				,		**snake_case_)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  get_size_dict(snake_case_)
							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(snake_case_				,		size=(size['''height'''], size['''width'''])				,		data_format=snake_case_				,		**snake_case_)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       List[str]:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a = None				,		**__a				,		)       ->       Union[str, Any]:






							'''simple docstring'''
							return normalize(snake_case_				,		mean=snake_case_				,		std=snake_case_				,		data_format=snake_case_				,		**snake_case_)




		def 						UpperCAmelCase							(					self				,		__a				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = ChannelDimension.FIRST				,		**__a				,		)       ->       Optional[int]:






							'''simple docstring'''
							_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(snake_case_				,		param_name='''size'''				,		default_to_square=snake_case_)
							_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(snake_case_				,		param_name='''crop_size'''				,		default_to_square=snake_case_)
							_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(snake_case_)

							if not valid_images(snake_case_):
												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(snake_case_) for image in images]

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

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

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

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

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

							_UpperCamelCase							 =  [to_channel_dimension_format(snake_case_				,		snake_case_) for image in images]

							_UpperCamelCase							 =  {'''pixel_values''': images}
							return BatchFeature(data=snake_case_				,		tensor_type=snake_case_)








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__       ={
    '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,
}
lowercase__       ='ETAOINSHRDLCUMWFGYPBVKJXQZ'
lowercase__       ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  List[Any]						=  {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    __UpperCamelCase     (				lowerCAmelCase__							:				tuple  ):
 return x[0]




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  Optional[Any]						=  get_letter_count(lowerCAmelCase__  )
 __a :  dict[int, list[str]]						=  {
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase__  )

 __a :  dict[int, str]						=  {}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find  ,   reverse=lowerCAmelCase__  )
  __a :  int						=  ''''''.join(freq_to_letter[freq]  )

 __a :  Any						=  list(freq_to_letter_str.items()  )
 freq_pairs.sort(key=lowerCAmelCase__  ,   reverse=lowerCAmelCase__  )

 __a :  list[str]						=  [freq_pair[1] for freq_pair in freq_pairs]

 return "".join(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  int						=  get_frequency_order(lowerCAmelCase__  )
 __a :  str						=  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()
