




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class     A					(				unittest.TestCase       ):







							@property
							def        lowercase__							(    self   :	List[str]				)		->					Optional[int]:

										"""simple docstring"""



										torch.manual_seed(0				)
										UpperCamelCase_     				= UNetaDModel(
										    block_out_channels=(32, 64)   ,     layers_per_block=2   ,     sample_size=32   ,     in_channels=3   ,     out_channels=3   ,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,     )
										return model







							def        lowercase__							(    self   :	Tuple				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= 'cpu'
										UpperCamelCase_     				= self.dummy_uncond_unet
										UpperCamelCase_     				= DDPMScheduler()

										UpperCamelCase_     				= DDPMPipeline(unet=__UpperCAmelCase   ,     scheduler=__UpperCAmelCase				)
										ddpm.to(__UpperCAmelCase				)
										ddpm.set_progress_bar_config(disable=__UpperCAmelCase				)

										UpperCamelCase_     				= torch.Generator(device=__UpperCAmelCase				).manual_seed(0				)
										UpperCamelCase_     				= ddpm(generator=__UpperCAmelCase   ,     num_inference_steps=2   ,     output_type='numpy'				).images

										UpperCamelCase_     				= torch.Generator(device=__UpperCAmelCase				).manual_seed(0				)
										UpperCamelCase_     				= ddpm(generator=__UpperCAmelCase   ,     num_inference_steps=2   ,     output_type='numpy'   ,     return_dict=__UpperCAmelCase				)[0]

										UpperCamelCase_     				= image[0, -3:, -3:, -1]
										UpperCamelCase_     				= image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase_     				= np.array(
										    [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







							def        lowercase__							(    self   :	Tuple				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.dummy_uncond_unet
										UpperCamelCase_     				= DDPMScheduler(prediction_type='sample'				)

										UpperCamelCase_     				= DDPMPipeline(unet=__UpperCAmelCase   ,     scheduler=__UpperCAmelCase				)
										ddpm.to(__UpperCAmelCase				)
										ddpm.set_progress_bar_config(disable=__UpperCAmelCase				)

										UpperCamelCase_     				= torch.manual_seed(0				)
										UpperCamelCase_     				= ddpm(generator=__UpperCAmelCase   ,     num_inference_steps=2   ,     output_type='numpy'				).images

										UpperCamelCase_     				= torch.manual_seed(0				)
										UpperCamelCase_     				= ddpm(generator=__UpperCAmelCase   ,     num_inference_steps=2   ,     output_type='numpy'				)[0]

										UpperCamelCase_     				= image[0, -3:, -3:, -1]
										UpperCamelCase_     				= image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase_     				= 1E-2 if torch_device != 'mps' else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()				).max() < tolerance







@slow
@require_torch_gpu
class     A					(				unittest.TestCase       ):







							def        lowercase__							(    self   :	Tuple				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= 'google/ddpm-cifar10-32'

										UpperCamelCase_     				= UNetaDModel.from_pretrained(__UpperCAmelCase				)
										UpperCamelCase_     				= DDPMScheduler.from_pretrained(__UpperCAmelCase				)

										UpperCamelCase_     				= DDPMPipeline(unet=__UpperCAmelCase   ,     scheduler=__UpperCAmelCase				)
										ddpm.to(__UpperCAmelCase				)
										ddpm.set_progress_bar_config(disable=__UpperCAmelCase				)

										UpperCamelCase_     				= torch.manual_seed(0				)
										UpperCamelCase_     				= ddpm(generator=__UpperCAmelCase   ,     output_type='numpy'				).images

										UpperCamelCase_     				= image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase_     				= np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]				)
										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2






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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor




class     A					(				unittest.TestCase       ):







							def __init__(    self   :	Any   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	List[Any]=7   ,     __UpperCAmelCase   :	Union[str, Any]=3   ,     __UpperCAmelCase   :	Optional[Any]=18   ,     __UpperCAmelCase   :	List[str]=30   ,     __UpperCAmelCase   :	int=400   ,     __UpperCAmelCase   :	List[Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     __UpperCAmelCase   :	List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     __UpperCAmelCase   :	Dict=True   ,     )		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= size if size is not None else {'shortest_edge': 20}
										UpperCamelCase_     				= crop_size if crop_size is not None else {'height': 18, 'width': 18}
										UpperCamelCase_     				= parent
										UpperCamelCase_     				= batch_size
										UpperCamelCase_     				= num_channels
										UpperCamelCase_     				= image_size
										UpperCamelCase_     				= min_resolution
										UpperCamelCase_     				= max_resolution
										UpperCamelCase_     				= do_resize
										UpperCamelCase_     				= size
										UpperCamelCase_     				= do_center_crop
										UpperCamelCase_     				= crop_size
										UpperCamelCase_     				= do_normalize
										UpperCamelCase_     				= image_mean
										UpperCamelCase_     				= image_std
										UpperCamelCase_     				= do_convert_rgb







							def        lowercase__							(    self   :	Tuple				)		->					List[Any]:

										"""simple docstring"""



										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	str=False   ,     __UpperCAmelCase   :	Tuple=False   ,     __UpperCAmelCase   :	Optional[int]=False				)		->					Any:

										"""simple docstring"""



										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
													UpperCamelCase_     				= []
													for i in range(self.batch_size				):
																image_inputs.append(
																    np.random.randint(
																        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta				)				)
										else:
													UpperCamelCase_     				= []
													for i in range(self.batch_size				):
																UpperCamelCase_				,   UpperCamelCase_     				= np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution				)   ,     2				)
																image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta				)				)

										if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													UpperCamelCase_     				= [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,     0   ,     -1				)				) for x in image_inputs]

										if torchify:
													UpperCamelCase_     				= [torch.from_numpy(__UpperCAmelCase				) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		Any			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self				)







							@property
							def        lowercase__							(    self   :	Optional[int]				)		->					Optional[Any]:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Tuple				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 20}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 18, 'width': 18}				)

										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42   ,     crop_size=84				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 42}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 84, 'width': 84}				)







							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[int]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	int				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







							def        lowercase__							(    self   :	Optional[Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     numpify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     np.ndarray				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







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

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     torchify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     torch.Tensor				)

										# Test not batched input
										UpperCamelCase_     				= 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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	str				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self   ,     num_channels=4				)
										UpperCamelCase_     				= 3







							@property
							def        lowercase__							(    self   :	Dict				)		->					str:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Optional[int]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	int				)		->					Any:

										"""simple docstring"""



										pass







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

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )
