






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 GLPNImageProcessor


class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=7     ,						_UpperCamelCase=3     ,						_UpperCamelCase=18     ,						_UpperCamelCase=30     ,						_UpperCamelCase=4_00     ,						_UpperCamelCase=True     ,						_UpperCamelCase=32     ,						_UpperCamelCase=True     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						parent
												lowerCAmelCase__    						=						batch_size
												lowerCAmelCase__    						=						num_channels
												lowerCAmelCase__    						=						image_size
												lowerCAmelCase__    						=						min_resolution
												lowerCAmelCase__    						=						max_resolution
												lowerCAmelCase__    						=						do_resize
												lowerCAmelCase__    						=						size_divisor
												lowerCAmelCase__    						=						do_rescale







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return {
												    "do_resize": self.do_resize,
												    "size_divisor": self.size_divisor,
												    "do_rescale": self.do_rescale,
												}




@require_torch
@require_vision
class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					GLPNImageProcessor if is_vision_available() else None






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						GLPNImageProcessingTester(self   )






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.image_processor_tester.prepare_image_processor_dict()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'do_resize'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'size_divisor'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'resample'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'do_rescale'   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__    						=						self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												lowerCAmelCase__    						=						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_UpperCamelCase   )
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase     ,						Image.Image   )

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowerCAmelCase__    						=						image_processing(image_inputs[0]     ,						return_tensors='pt'   ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__    						=						self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												lowerCAmelCase__    						=						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_UpperCamelCase     ,						numpify=_UpperCamelCase   )
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase     ,						np.ndarray   )

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowerCAmelCase__    						=						image_processing(image_inputs[0]     ,						return_tensors='pt'   ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__    						=						self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												lowerCAmelCase__    						=						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_UpperCamelCase     ,						torchify=_UpperCamelCase   )
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase     ,						torch.Tensor   )

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowerCAmelCase__    						=						image_processing(image_inputs[0]     ,						return_tensors='pt'   ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )













def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_    ,       UpperCamelCase_		):
												raise ValueError('multiplicative_persistence() only accepts integral values'		)
						if num < 0:
												raise ValueError('multiplicative_persistence() does not accept negative values'		)

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						str(UpperCamelCase_		)

						while len(UpperCamelCase_		) != 1:
												lowerCAmelCase__    						=						[int(UpperCamelCase_		) for i in num_string]

												lowerCAmelCase__    						=						1
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		total *= numbers[i]

												lowerCAmelCase__    						=						str(UpperCamelCase_		)

												steps += 1
						return steps







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_    ,       UpperCamelCase_		):
												raise ValueError('additive_persistence() only accepts integral values'		)
						if num < 0:
												raise ValueError('additive_persistence() does not accept negative values'		)

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						str(UpperCamelCase_		)

						while len(UpperCamelCase_		) != 1:
												lowerCAmelCase__    						=						[int(UpperCamelCase_		) for i in num_string]

												lowerCAmelCase__    						=						0
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		total += numbers[i]

												lowerCAmelCase__    						=						str(UpperCamelCase_		)

												steps += 1
						return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()





