


import random
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():
		import PIL

		from transformers import FlavaImageProcessor
		from transformers.image_utils import PILImageResampling
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)
else:
		lowerCamelCase		=	lowerCamelCase		=	lowerCamelCase		=	lowerCamelCase		=	None





class       _a							(       unittest.TestCase):




	def __init__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	Tuple=7	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=3	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=30	,      _SCREAMING_SNAKE_CASE		:	str=400	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=True	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=None	,      _SCREAMING_SNAKE_CASE		:	str=True	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=None	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      _SCREAMING_SNAKE_CASE		:	Dict=True	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=1 / 255	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=True	,      _SCREAMING_SNAKE_CASE		:	Any=FLAVA_IMAGE_MEAN	,      _SCREAMING_SNAKE_CASE		:	List[Any]=FLAVA_IMAGE_STD	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=14	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=75	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=None	,      _SCREAMING_SNAKE_CASE		:	int=16	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=0.3	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=None	,      _SCREAMING_SNAKE_CASE		:	str=True	,      _SCREAMING_SNAKE_CASE		:	int=None	,      _SCREAMING_SNAKE_CASE		:	Any=None	,      _SCREAMING_SNAKE_CASE		:	int=True	,      _SCREAMING_SNAKE_CASE		:	Tuple=None	,      _SCREAMING_SNAKE_CASE		:	str=True	,      _SCREAMING_SNAKE_CASE		:	str=True	,      _SCREAMING_SNAKE_CASE		:	Tuple=FLAVA_CODEBOOK_MEAN	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=FLAVA_CODEBOOK_STD	,      )-> Tuple:
			lowerCAmelCase__     :    Optional[int]					  =      size if size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase__     :    Dict					  =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase__     :    List[str]					  =      codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
			lowerCAmelCase__     :    int					  =      codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

			lowerCAmelCase__     :    int					  =      parent
			lowerCAmelCase__     :    str					  =      batch_size
			lowerCAmelCase__     :    int					  =      num_channels
			lowerCAmelCase__     :    str					  =      do_resize
			lowerCAmelCase__     :    Dict					  =      do_rescale
			lowerCAmelCase__     :    Tuple					  =      rescale_factor
			lowerCAmelCase__     :    Optional[Any]					  =      min_resolution
			lowerCAmelCase__     :    str					  =      max_resolution
			lowerCAmelCase__     :    Optional[int]					  =      size
			lowerCAmelCase__     :    Any					  =      resample if resample is not None else PILImageResampling.BICUBIC
			lowerCAmelCase__     :    Tuple					  =      do_normalize
			lowerCAmelCase__     :    Any					  =      image_mean
			lowerCAmelCase__     :    Dict					  =      image_std
			lowerCAmelCase__     :    Union[str, Any]					  =      do_center_crop
			lowerCAmelCase__     :    Union[str, Any]					  =      crop_size

			lowerCAmelCase__     :    Union[str, Any]					  =      input_size_patches
			lowerCAmelCase__     :    Tuple					  =      total_mask_patches
			lowerCAmelCase__     :    Dict					  =      mask_group_max_patches
			lowerCAmelCase__     :    List[Any]					  =      mask_group_min_patches
			lowerCAmelCase__     :    Dict					  =      mask_group_min_aspect_ratio
			lowerCAmelCase__     :    Tuple					  =      mask_group_max_aspect_ratio

			lowerCAmelCase__     :    int					  =      codebook_do_resize
			lowerCAmelCase__     :    int					  =      codebook_size
			lowerCAmelCase__     :    int					  =      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
			lowerCAmelCase__     :    Dict					  =      codebook_do_center_crop
			lowerCAmelCase__     :    Union[str, Any]					  =      codebook_crop_size
			lowerCAmelCase__     :    Optional[int]					  =      codebook_do_map_pixels
			lowerCAmelCase__     :    List[Any]					  =      codebook_do_normalize
			lowerCAmelCase__     :    Optional[Any]					  =      codebook_image_mean
			lowerCAmelCase__     :    Tuple					  =      codebook_image_std




	def 							UpperCAmelCase__(      self		:	Dict	)-> Optional[Any]:
			return {
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_normalize": self.do_normalize,
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "resample": self.resample,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "input_size_patches": self.input_size_patches,
			    "total_mask_patches": self.total_mask_patches,
			    "mask_group_max_patches": self.mask_group_max_patches,
			    "mask_group_min_patches": self.mask_group_min_patches,
			    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
			    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
			    "codebook_do_resize": self.codebook_do_resize,
			    "codebook_size": self.codebook_size,
			    "codebook_resample": self.codebook_resample,
			    "codebook_do_center_crop": self.codebook_do_center_crop,
			    "codebook_crop_size": self.codebook_crop_size,
			    "codebook_do_map_pixels": self.codebook_do_map_pixels,
			    "codebook_do_normalize": self.codebook_do_normalize,
			    "codebook_image_mean": self.codebook_image_mean,
			    "codebook_image_std": self.codebook_image_std,
			}




	def 							UpperCAmelCase__(      self		:	List[Any]	)-> Dict:
			return (self.size["height"], self.size["width"])




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Optional[Any]:
			return (
			    (self.input_size_patches, self.input_size_patches)
			    if not isinstance(self.input_size_patches	,      _SCREAMING_SNAKE_CASE	)
			    else self.input_size_patches
			)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> Optional[int]:
			return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class       _a							(       _lowercase  ,		unittest.TestCase):
	_a						:						Any   					=							FlavaImageProcessor if is_vision_available() else None
	_a						:						Any   					=							None




	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> int:
			lowerCAmelCase__     :    List[str]					  =      FlavaImageProcessingTester(self	)




	@property
	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Tuple:
			return self.image_processor_tester.prepare_image_processor_dict()




	def 							UpperCAmelCase__(      self		:	str	)-> int:
			lowerCAmelCase__     :    Optional[int]					  =      self.image_processing_class(**self.image_processor_dict	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''image_mean'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''image_std'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''do_normalize'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''do_resize'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''resample'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''crop_size'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''do_center_crop'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''do_rescale'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''rescale_factor'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''masking_generator'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_do_resize'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_size'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_resample'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_do_center_crop'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_crop_size'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_do_map_pixels'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_do_normalize'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_image_mean'''	)	)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,      '''codebook_image_std'''	)	)




	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> int:
			lowerCAmelCase__     :    str					  =      self.image_processing_class.from_dict(self.image_processor_dict	)
			self.assertEqual(image_processor.size	,      {'''height''': 224, '''width''': 224}	)
			self.assertEqual(image_processor.crop_size	,      {'''height''': 224, '''width''': 224}	)
			self.assertEqual(image_processor.codebook_size	,      {'''height''': 112, '''width''': 112}	)
			self.assertEqual(image_processor.codebook_crop_size	,      {'''height''': 112, '''width''': 112}	)

			lowerCAmelCase__     :    Optional[int]					  =      self.image_processing_class.from_dict(
			    self.image_processor_dict	,      size=42	,      crop_size=84	,      codebook_size=33	,      codebook_crop_size=66	)
			self.assertEqual(image_processor.size	,      {'''height''': 42, '''width''': 42}	)
			self.assertEqual(image_processor.crop_size	,      {'''height''': 84, '''width''': 84}	)
			self.assertEqual(image_processor.codebook_size	,      {'''height''': 33, '''width''': 33}	)
			self.assertEqual(image_processor.codebook_crop_size	,      {'''height''': 66, '''width''': 66}	)




	def 							UpperCAmelCase__(      self		:	str	)-> Optional[Any]:
			pass




	def 							UpperCAmelCase__(      self		:	int	)-> Any:
			# Initialize image_processing
			lowerCAmelCase__     :    Union[str, Any]					  =      self.image_processing_class(**self.image_processor_dict	)
			# create random PIL images
			lowerCAmelCase__     :    Union[str, Any]					  =      prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_SCREAMING_SNAKE_CASE	)
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE	,      PIL.Image.Image	)

			# Test not batched input
			lowerCAmelCase__     :    Any					  =      image_processing(image_inputs[0]	,      return_tensors='''pt'''	)

			# Test no bool masked pos
			self.assertFalse('''bool_masked_pos''' in encoded_images	)

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.image_processor_tester.get_expected_image_size()

			self.assertEqual(
			    encoded_images.pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

			# Test batched
			lowerCAmelCase__     :    Union[str, Any]					  =      image_processing(_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)
			lowerCAmelCase__  ,			lowerCAmelCase__     :    Dict					  =      self.image_processor_tester.get_expected_image_size()

			# Test no bool masked pos
			self.assertFalse('''bool_masked_pos''' in encoded_images	)

			self.assertEqual(
			    encoded_images.pixel_values.shape	,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )	,      )




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Any	,      _SCREAMING_SNAKE_CASE		:	List[str]	)-> Tuple:
			# Initialize image_processing
			lowerCAmelCase__     :    int					  =      self.image_processing_class(**self.image_processor_dict	)
			# create random tensors
			lowerCAmelCase__     :    List[str]					  =      prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	)
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)

			# Test not batched input
			lowerCAmelCase__     :    Dict					  =      image_processing(image_inputs[0]	,      return_tensors='''pt'''	)

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Dict					  =      self.image_processor_tester.get_expected_image_size()
			self.assertEqual(
			    encoded_images.pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

			lowerCAmelCase__     :    List[Any]					  =      image_processing(_SCREAMING_SNAKE_CASE	,      return_image_mask=_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.image_processor_tester.get_expected_image_size()
			self.assertEqual(
			    encoded_images.pixel_values.shape	,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )	,      )

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.image_processor_tester.get_expected_mask_size()
			self.assertEqual(
			    encoded_images.bool_masked_pos.shape	,      (
			        self.image_processor_tester.batch_size,
			        expected_height,
			        expected_width,
			    )	,      )

			# Test batched
			lowerCAmelCase__     :    Any					  =      image_processing(_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	).pixel_values

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Union[str, Any]					  =      self.image_processor_tester.get_expected_image_size()
			self.assertEqual(
			    encoded_images.shape	,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )	,      )

			# Test masking
			lowerCAmelCase__     :    Optional[int]					  =      image_processing(_SCREAMING_SNAKE_CASE	,      return_image_mask=_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)

			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.image_processor_tester.get_expected_image_size()
			self.assertEqual(
			    encoded_images.pixel_values.shape	,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )	,      )

			lowerCAmelCase__  ,			lowerCAmelCase__     :    List[Any]					  =      self.image_processor_tester.get_expected_mask_size()
			self.assertEqual(
			    encoded_images.bool_masked_pos.shape	,      (
			        self.image_processor_tester.batch_size,
			        expected_height,
			        expected_width,
			    )	,      )




	def 							UpperCAmelCase__(      self		:	Tuple	)-> List[str]:
			self._test_call_framework(np.ndarray	,      prepare_kwargs={'''numpify''': True}	)




	def 							UpperCAmelCase__(      self		:	Dict	)-> List[Any]:
			self._test_call_framework(torch.Tensor	,      prepare_kwargs={'''torchify''': True}	)




	def 							UpperCAmelCase__(      self		:	Any	)-> Tuple:
			# Initialize image_processing
			random.seed(1234	)
			lowerCAmelCase__     :    Any					  =      self.image_processing_class(**self.image_processor_dict	)
			lowerCAmelCase__     :    List[Any]					  =      prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_SCREAMING_SNAKE_CASE	,      torchify=_SCREAMING_SNAKE_CASE	)

			# Test not batched input
			lowerCAmelCase__     :    List[Any]					  =      image_processing(image_inputs[0]	,      return_image_mask=_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)
			self.assertEqual(encoded_images.bool_masked_pos.sum().item()	,      75	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> str:
			# Initialize image_processing
			lowerCAmelCase__     :    Optional[int]					  =      self.image_processing_class(**self.image_processor_dict	)
			# create random PIL images
			lowerCAmelCase__     :    Any					  =      prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_SCREAMING_SNAKE_CASE	)
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE	,      PIL.Image.Image	)

			# Test not batched input
			lowerCAmelCase__     :    List[str]					  =      image_processing(image_inputs[0]	,      return_codebook_pixels=_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)
			lowerCAmelCase__  ,			lowerCAmelCase__     :    int					  =      self.image_processor_tester.get_expected_codebook_image_size()
			self.assertEqual(
			    encoded_images.codebook_pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

			# Test batched
			lowerCAmelCase__     :    int					  =      image_processing(_SCREAMING_SNAKE_CASE	,      return_codebook_pixels=_SCREAMING_SNAKE_CASE	,      return_tensors='''pt'''	)
			lowerCAmelCase__  ,			lowerCAmelCase__     :    Dict					  =      self.image_processor_tester.get_expected_codebook_image_size()
			self.assertEqual(
			    encoded_images.codebook_pixel_values.shape	,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )	,      )








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 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    int					  =      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 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			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 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      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 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




