






'''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 _lowercase			(       unittest.TestCase	):




							def __init__(							self:       Any    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Union[str, Any]=7    ,							UpperCamelCase__:       Dict=3    ,							UpperCamelCase__:       str=18    ,							UpperCamelCase__:       str=30    ,							UpperCamelCase__:       str=400    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       Optional[Any]=32    ,							UpperCamelCase__:       Union[str, Any]=True    ,							):
														lowerCamelCase__      :			List[Any] 						= parent
														lowerCamelCase__      :			Union[str, Any] 						= batch_size
														lowerCamelCase__      :			Dict 						= num_channels
														lowerCamelCase__      :			Tuple 						= image_size
														lowerCamelCase__      :			Dict 						= min_resolution
														lowerCamelCase__      :			List[Any] 						= max_resolution
														lowerCamelCase__      :			Union[str, Any] 						= do_resize
														lowerCamelCase__      :			int 						= size_divisor
														lowerCamelCase__      :			List[Any] 						= do_rescale




							def 	lowerCamelCase_       (							self:       List[str]  ):
														return {
														    "do_resize": self.do_resize,
														    "size_divisor": self.size_divisor,
														    "do_rescale": self.do_rescale,
														}



@require_torch
@require_vision
class _lowercase			(       _lowercase       ,   unittest.TestCase	):
							a          = GLPNImageProcessor if is_vision_available() else None




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[str] 						= GLPNImageProcessingTester(self  )




							@property
							def 	lowerCamelCase_       (							self:       Dict  ):
														return self.image_processor_tester.prepare_image_processor_dict()




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Dict 						= 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 	lowerCamelCase_       (							self:       List[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														# Initialize image_processing
														lowerCamelCase__      :			Union[str, Any] 						= self.image_processing_class(**self.image_processor_dict  )
														# create random PIL images
														lowerCamelCase__      :			Optional[Any] 						= 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__      :			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 	lowerCamelCase_       (							self:       int  ):
														# Initialize image_processing
														lowerCamelCase__      :			Dict 						= self.image_processing_class(**self.image_processor_dict  )
														# create random numpy tensors
														lowerCamelCase__      :			str 						= 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__      :			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 	lowerCamelCase_       (							self:       List[Any]  ):
														# Initialize image_processing
														lowerCamelCase__      :			Tuple 						= self.image_processing_class(**self.image_processor_dict  )
														# create random PyTorch tensors
														lowerCamelCase__      :			List[str] 						= 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__      :			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  )









'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

