code: 
  
  """simple docstring"""
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
  			import torch
  
  if is_vision_available():
  			from PIL import Image
  
  			from transformers import DeiTImageProcessor
  
  
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
  							def __init__(      self ,						a_ ,						a_=7 ,						a_=3 ,						a_=18 ,						a_=30 ,						a_=400 ,						a_=True ,						a_=None ,						a_=True ,						a_=None ,						a_=True ,						a_=[0.5, 0.5, 0.5] ,						a_=[0.5, 0.5, 0.5] ,						)							->							Union[str, Any]:
  								_UpperCAmelCase =						size if size is not None else {"height": 20, "width": 20}
  								_UpperCAmelCase =						crop_size if crop_size is not None else {"height": 18, "width": 18}
  
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						num_channels
  								_UpperCAmelCase =						image_size
  								_UpperCAmelCase =						min_resolution
  								_UpperCAmelCase =						max_resolution
  								_UpperCAmelCase =						do_resize
  								_UpperCAmelCase =						size
  								_UpperCAmelCase =						do_center_crop
  								_UpperCAmelCase =						crop_size
  								_UpperCAmelCase =						do_normalize
  								_UpperCAmelCase =						image_mean
  								_UpperCAmelCase =						image_std
  
  
  
  							def       _a   (      self   )							->							Dict:
  								return {
  								    "do_resize": self.do_resize,
  								    "size": self.size,
  								    "do_center_crop": self.do_center_crop,
  								    "crop_size": self.crop_size,
  								    "do_normalize": self.do_normalize,
  								    "image_mean": self.image_mean,
  								    "image_std": self.image_std,
  								}
  
  
  
  
  
  @require_torch
  @require_vision
  class    _lowerCAmelCase						(						lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     Optional[int]       =							DeiTImageProcessor if is_vision_available() else None
  							lowercase_     :     Union[str, Any]       =							True
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						DeiTImageProcessingTester(self   )
  
  
  
  							@property
  							def       _a   (      self   )							->							Tuple:
  								return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								self.assertTrue(hasattr(a_ ,						"do_resize"   )   )
  								self.assertTrue(hasattr(a_ ,						"size"   )   )
  								self.assertTrue(hasattr(a_ ,						"do_center_crop"   )   )
  								self.assertTrue(hasattr(a_ ,						"center_crop"   )   )
  								self.assertTrue(hasattr(a_ ,						"do_normalize"   )   )
  								self.assertTrue(hasattr(a_ ,						"image_mean"   )   )
  								self.assertTrue(hasattr(a_ ,						"image_std"   )   )
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						self.image_processing_class.from_dict(self.image_processor_dict   )
  								self.assertEqual(image_processor.size ,						{"height": 20, "width": 20}   )
  								self.assertEqual(image_processor.crop_size ,						{"height": 18, "width": 18}   )
  
  								_UpperCAmelCase =						self.image_processing_class.from_dict(self.image_processor_dict ,						size=42 ,						crop_size=84   )
  								self.assertEqual(image_processor.size ,						{"height": 42, "width": 42}   )
  								self.assertEqual(image_processor.crop_size ,						{"height": 84, "width": 84}   )
  
  
  
  							def       _a   (      self   )							->							Dict:
  								pass
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random PIL images
  								_UpperCAmelCase =						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a_   )
  								for image in image_inputs:
  									self.assertIsInstance(a_ ,						Image.Image   )
  
  								# Test not batched input
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        1,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  
  
  							def       _a   (      self   )							->							int:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random numpy tensors
  								_UpperCAmelCase =						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a_ ,						numpify=a_   )
  								for image in image_inputs:
  									self.assertIsInstance(a_ ,						np.ndarray   )
  
  								# Test not batched input
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        1,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								# Initialize image_processing
  								_UpperCAmelCase =						self.image_processing_class(**self.image_processor_dict   )
  								# create random PyTorch tensors
  								_UpperCAmelCase =						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a_ ,						torchify=a_   )
  								for image in image_inputs:
  									self.assertIsInstance(a_ ,						torch.Tensor   )
  
  								# Test not batched input
  								_UpperCAmelCase =						image_processing(image_inputs[0] ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        1,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  								# Test batched
  								_UpperCAmelCase =						image_processing(a_ ,						return_tensors="pt"   ).pixel_values
  								self.assertEqual(
  								    encoded_images.shape ,						(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size["height"],
  								        self.image_processor_tester.crop_size["width"],
  								    ) ,						)
  
  
  
  
  
style_context: 
  """simple docstring"""
  from collections import namedtuple
  
  __magic_name__		       =				namedtuple('''from_to''', '''from_ to''')
  
  __magic_name__		       =				{
      '''atm''': from_to(1, 1),
      '''pascal''': from_to(0.0000098, 10_13_25),
      '''bar''': from_to(0.986923, 1.01325),
      '''kilopascal''': from_to(0.00986923, 101.325),
      '''megapascal''': from_to(9.86923, 0.101325),
      '''psi''': from_to(0.068046, 14.6959),
      '''inHg''': from_to(0.0334211, 29.9213),
      '''torr''': from_to(0.00131579, 7_60),
  }
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if from_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	if to_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	return (
  	    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  	)
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  