




"""simple docstring"""
import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=7						,	__a=3						,	__a=18						,	__a=30						,	__a=400						,	__a=True						,	__a=None						,	__a=True						,	__a=[0.5, 0.5, 0.5]						,	__a=[0.5, 0.5, 0.5]						,	):
								__lowerCamelCase					:	str	       =				size if size is not None else {'height': 18, 'width': 18}
								__lowerCamelCase					:	Any	       =				parent
								__lowerCamelCase					:	Optional[int]	       =				batch_size
								__lowerCamelCase					:	Dict	       =				num_channels
								__lowerCamelCase					:	int	       =				image_size
								__lowerCamelCase					:	str	       =				min_resolution
								__lowerCamelCase					:	int	       =				max_resolution
								__lowerCamelCase					:	Dict	       =				do_resize
								__lowerCamelCase					:	Optional[Any]	       =				size
								__lowerCamelCase					:	Optional[int]	       =				do_normalize
								__lowerCamelCase					:	str	       =				image_mean
								__lowerCamelCase					:	List[str]	       =				image_std





					def     snake_case_				(    self     ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}



@require_torch
@require_vision
class __lowercase(      _a	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= DPTImageProcessor if is_vision_available() else None
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				DPTImageProcessingTester(self     )
					@property
					def     snake_case_				(    self     ):
								return self.image_processor_tester.prepare_image_processor_dict()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case_						,	'image_mean'     )     )
								self.assertTrue(hasattr(snake_case_						,	'image_std'     )     )
								self.assertTrue(hasattr(snake_case_						,	'do_normalize'     )     )
								self.assertTrue(hasattr(snake_case_						,	'do_resize'     )     )
								self.assertTrue(hasattr(snake_case_						,	'size'     )     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size						,	{'height': 18, 'width': 18}     )

								__lowerCamelCase					:	Any	       =				self.image_processing_class.from_dict(self.image_processor_dict						,	size=42     )
								self.assertEqual(image_processor.size						,	{'height': 42, 'width': 42}     )
					def     snake_case_				(    self     ):
								# Initialize image_processing
								__lowerCamelCase					:	List[Any]	       =				self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								__lowerCamelCase					:	List[str]	       =				prepare_image_inputs(self.image_processor_tester						,	equal_resolution=snake_case_     )
								for image in image_inputs:
											self.assertIsInstance(snake_case_						,	Image.Image     )

								# Test not batched input
								__lowerCamelCase					:	str	       =				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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)

								# Test batched
								__lowerCamelCase					:	List[Any]	       =				image_processing(snake_case_						,	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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)
					def     snake_case_				(    self     ):
								# Initialize image_processing
								__lowerCamelCase					:	Optional[Any]	       =				self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								__lowerCamelCase					:	int	       =				prepare_image_inputs(self.image_processor_tester						,	equal_resolution=snake_case_						,	numpify=snake_case_     )
								for image in image_inputs:
											self.assertIsInstance(snake_case_						,	np.ndarray     )

								# Test not batched input
								__lowerCamelCase					:	Union[str, Any]	       =				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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)

								# Test batched
								__lowerCamelCase					:	int	       =				image_processing(snake_case_						,	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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)





					def     snake_case_				(    self     ):
								# Initialize image_processing
								__lowerCamelCase					:	Optional[int]	       =				self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								__lowerCamelCase					:	Any	       =				prepare_image_inputs(self.image_processor_tester						,	equal_resolution=snake_case_						,	torchify=snake_case_     )
								for image in image_inputs:
											self.assertIsInstance(snake_case_						,	torch.Tensor     )

								# Test not batched input
								__lowerCamelCase					:	Dict	       =				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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)

								# Test batched
								__lowerCamelCase					:	int	       =				image_processing(snake_case_						,	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.size['height'],
								        self.image_processor_tester.size['width'],
								    )						,	)









"""simple docstring"""
a_				:    Dict										=     {
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
a_				:    Optional[int]										=     {
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def 						UpperCAmelCase				(			A__:    float							,				A__:    str							,				A__:    str     )  ->     float:
			__lowerCamelCase					:	List[Any]	       =				from_type.lower().strip('s'     )
			__lowerCamelCase					:	Optional[int]	       =				to_type.lower().strip('s'     )

			__lowerCamelCase					:	int	       =				UNIT_SYMBOL.get(A__							,				A__     )
			__lowerCamelCase					:	Dict	       =				UNIT_SYMBOL.get(A__							,				A__     )

			if from_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Dict	       =				(
						    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			if to_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Any	       =				(
						    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			__lowerCamelCase					:	Any	       =				METRIC_CONVERSION[from_sanitized]
			__lowerCamelCase					:	Tuple	       =				METRIC_CONVERSION[to_sanitized]
			__lowerCamelCase					:	Any	       =				1

			if from_exponent > to_exponent:
						__lowerCamelCase					:	Union[str, Any]	       =				from_exponent - to_exponent
			else:
						__lowerCamelCase					:	List[Any]	       =				-(to_exponent - from_exponent)

			return value * pow(10							,				A__     )


if __name__ == "__main__":
					from doctest import testmod

					testmod()



