

"""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"],
								    ) ,						)






"""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()




