




"""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 GLPNImageProcessor






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=7		, _a=3		, _a=18		, _a=30		, _a=400		, _a=True		, _a=32		, _a=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    _lowerCAmelCase				(       self ):





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






@require_torch
@require_vision
class     __magic_name__							(       _A						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						GLPNImageProcessor if is_vision_available() else None





				def    _lowerCAmelCase				(       self ):





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





				@property
				def    _lowerCAmelCase				(       self ):





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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(_a		, """do_resize""" ) )
							self.assertTrue(hasattr(_a		, """size_divisor""" ) )
							self.assertTrue(hasattr(_a		, """resample""" ) )
							self.assertTrue(hasattr(_a		, """do_rescale""" ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       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=_a )
							for image in image_inputs:
										self.assertIsInstance(_a		, 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    _lowerCAmelCase				(       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=_a		, numpify=_a )
							for image in image_inputs:
										self.assertIsInstance(_a		, 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    _lowerCAmelCase				(       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=_a		, torchify=_a )
							for image in image_inputs:
										self.assertIsInstance(_a		, 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 )











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



