





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 			_a						(				unittest.TestCase    ):






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   str=7,			lowerCAmelCase__		:   List[Any]=3,			lowerCAmelCase__		:   int=1_8,			lowerCAmelCase__		:   Union[str, Any]=3_0,			lowerCAmelCase__		:   Optional[Any]=4_0_0,			lowerCAmelCase__		:   int=True,			lowerCAmelCase__		:   List[str]=3_2,			lowerCAmelCase__		:   Optional[int]=True,			)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   parent
										_UpperCamelCase      : List[Any]         =   batch_size
										_UpperCamelCase      : Any         =   num_channels
										_UpperCamelCase      : int         =   image_size
										_UpperCamelCase      : Dict         =   min_resolution
										_UpperCamelCase      : Any         =   max_resolution
										_UpperCamelCase      : Optional[Any]         =   do_resize
										_UpperCamelCase      : Dict         =   size_divisor
										_UpperCamelCase      : List[str]         =   do_rescale






				def     snake_case     (			self		:   Optional[Any]							)     ->				Any:




										'''simple docstring'''



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




@require_torch
@require_vision
class 			_a						(				_lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   GLPNImageProcessor if is_vision_available() else None






				def     snake_case     (			self		:   Any							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   GLPNImageProcessingTester(self							)






				@property
				def     snake_case     (			self		:   Dict							)     ->				Any:




										'''simple docstring'''



										return self.image_processor_tester.prepare_image_processor_dict()






				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(lowerCAmelCase__,			'''do_resize'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__,			'''size_divisor'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__,			'''resample'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__,			'''do_rescale'''							)							)






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										pass






				def     snake_case     (			self		:   Tuple							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										_UpperCamelCase      : Union[str, Any]         =   prepare_image_inputs(self.image_processor_tester,			equal_resolution=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__,			Image.Image							)

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										_UpperCamelCase      : Tuple         =   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     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										_UpperCamelCase      : Optional[Any]         =   prepare_image_inputs(self.image_processor_tester,			equal_resolution=lowerCAmelCase__,			numpify=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__,			np.ndarray							)

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										_UpperCamelCase      : Union[str, Any]         =   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     snake_case     (			self		:   Any							)     ->				Tuple:




										'''simple docstring'''



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

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										_UpperCamelCase      : Optional[Any]         =   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"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)



