


import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=256       ):




     '''simple docstring'''





     __A  :					List[Any]						     =       min(img.size       )

     if s < target_image_size:
          raise ValueError(f"min dim for image {s} < {target_image_size}"       )

     __A  :					Optional[int]						     =       target_image_size / s
     __A  :					List[str]						     =       (round(r * img.size[1]       ), round(r * img.size[0]       ))
     __A  :					Union[str, Any]						     =       TF.resize(snake_case_					,				snake_case_					,				interpolation=PIL.Image.LANCZOS       )
     __A  :					Any						     =       TF.center_crop(snake_case_					,				output_size=2 * [target_image_size]       )
     __A  :					Optional[int]						     =       torch.unsqueeze(T.ToTensor()(snake_case_       )					,				0       )
     return img


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Tuple						     =       2.0 * x - 1.0
     return x


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=True					,				SCREAMING_SNAKE_CASE="RGB"       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       x.detach().cpu()
     if process:
          __A  :					Union[str, Any]						     =       post_process_tensor(snake_case_       )
     __A  :					List[Any]						     =       x.numpy()
     if process:
          __A  :					Any						     =       (255 * x).astype(np.uinta       )
     __A  :					str						     =       Image.fromarray(snake_case_       )
     if not x.mode == mode:
          __A  :					Tuple						     =       x.convert(snake_case_       )
     return x


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       torch.clamp(snake_case_					,				-1.0					,				1.0       )
     __A  :					Tuple						     =       (x + 1.0) / 2.0
     __A  :					Optional[Any]						     =       x.permute(1					,				2					,				0       )
     return x






def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       post_process_tensor(x.squeeze()       )
     return x.permute(2					,				0					,				1       ).unsqueeze(0       )





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

