code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import namedtuple
  
  lowercase_								=    namedtuple('from_to', 'from_ to')
  
  lowercase_								=    {
      'millimeter': 'mm',
      'centimeter': 'cm',
      'meter': 'm',
      'kilometer': 'km',
      'inch': 'in',
      'inche': 'in',  # Trailing 's' has been stripped off
      'feet': 'ft',
      'foot': 'ft',
      'yard': 'yd',
      'mile': 'mi',
  }
  
  lowercase_								=    {
      'mm': from_to(0.001, 10_00),
      'cm': from_to(0.01, 1_00),
      'm': from_to(1, 1),
      'km': from_to(10_00, 0.001),
      'in': from_to(0.0254, 39.3701),
      'ft': from_to(0.3048, 3.28084),
      'yd': from_to(0.9144, 1.09361),
      'mi': from_to(1609.34, 0.000621371),
  }
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  float:
  						_a     =			from_type.lower().rstrip('s'				)
  						_a     =			TYPE_CONVERSION.get(_UpperCAmelCase							,     _UpperCAmelCase				)
  						_a     =			to_type.lower().rstrip('s'				)
  						_a     =			TYPE_CONVERSION.get(_UpperCAmelCase							,     _UpperCAmelCase				)
  						if new_from not in METRIC_CONVERSION:
  												_a     =			(
  												    f"""Invalid 'from_type' value: {from_type!r}.\n"""
  												    f"""Conversion abbreviations are: {', '.join(_UpperCAmelCase				)}"""
  												)
  												raise ValueError(_UpperCAmelCase				)
  						if new_to not in METRIC_CONVERSION:
  												_a     =			(
  												    f"""Invalid 'to_type' value: {to_type!r}.\n"""
  												    f"""Conversion abbreviations are: {', '.join(_UpperCAmelCase				)}"""
  												)
  												raise ValueError(_UpperCAmelCase				)
  						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
style_context: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_a     =			min(img.size				)
  
  						if s < target_image_size:
  												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)
  
  						_a     =			target_image_size / s
  						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
  						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  