



"""simple docstring"""







from collections import namedtuple

UpperCAmelCase:  Tuple			     =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase:  Union[str, Any]			     =				{
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.0_000_098, 101_325),
    """bar""": from_to(0.986_923, 1.01_325),
    """kilopascal""": from_to(0.00_986_923, 101.325),
    """megapascal""": from_to(9.86_923, 0.101_325),
    """psi""": from_to(0.068_046, 14.6_959),
    """inHg""": from_to(0.0_334_211, 29.9_213),
    """torr""": from_to(0.00_131_579, 760),
}


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		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()








'''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				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								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__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




	def      lowercase__							(  self		:  str					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   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      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   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}					)

								lowerCAmelCase__  =   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      lowercase__							(  self		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		Image.Image					)

								# Test not batched input
								lowerCAmelCase__  =   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
								lowerCAmelCase__  =   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      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   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
								lowerCAmelCase__  =   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
								lowerCAmelCase__  =   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      lowercase__							(  self		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   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
								lowerCAmelCase__  =   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
								lowerCAmelCase__  =   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"""],
								    )   ,		)





