code: 
  '''simple docstring'''
  
  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  ):
  									torch.manual_seed(0  )
  									_lowerCAmelCase       :  str   				=					UNetaDModel(
  									    block_out_channels=(3_2, 6_4)   ,   layers_per_block=2   ,   sample_size=3_2   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,   )
  									return model
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  List[Any]   				=					"""cpu"""
  									_lowerCAmelCase       :  str   				=					self.dummy_uncond_unet
  									_lowerCAmelCase       :  List[Any]   				=					DDPMScheduler()
  
  									_lowerCAmelCase       :  Any   				=					DDPMPipeline(unet=__snake_case   ,   scheduler=__snake_case  )
  									ddpm.to(__snake_case  )
  									ddpm.set_progress_bar_config(disable=__snake_case  )
  
  									_lowerCAmelCase       :  List[Any]   				=					torch.Generator(device=__snake_case  ).manual_seed(0  )
  									_lowerCAmelCase       :  Union[str, Any]   				=					ddpm(generator=__snake_case   ,   num_inference_steps=2   ,   output_type="""numpy"""  ).images
  
  									_lowerCAmelCase       :  List[str]   				=					torch.Generator(device=__snake_case  ).manual_seed(0  )
  									_lowerCAmelCase       :  Optional[Any]   				=					ddpm(generator=__snake_case   ,   num_inference_steps=2   ,   output_type="""numpy"""   ,   return_dict=__snake_case  )[0]
  
  									_lowerCAmelCase       :  str   				=					image[0, -3:, -3:, -1]
  									_lowerCAmelCase       :  List[Any]   				=					image_from_tuple[0, -3:, -3:, -1]
  
  									assert image.shape == (1, 3_2, 3_2, 3)
  									_lowerCAmelCase       :  Tuple   				=					np.array(
  									    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-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  ):
  									_lowerCAmelCase       :  int   				=					self.dummy_uncond_unet
  									_lowerCAmelCase       :  Union[str, Any]   				=					DDPMScheduler(prediction_type="""sample"""  )
  
  									_lowerCAmelCase       :  Optional[Any]   				=					DDPMPipeline(unet=__snake_case   ,   scheduler=__snake_case  )
  									ddpm.to(__snake_case  )
  									ddpm.set_progress_bar_config(disable=__snake_case  )
  
  									_lowerCAmelCase       :  Optional[int]   				=					torch.manual_seed(0  )
  									_lowerCAmelCase       :  Optional[Any]   				=					ddpm(generator=__snake_case   ,   num_inference_steps=2   ,   output_type="""numpy"""  ).images
  
  									_lowerCAmelCase       :  Dict   				=					torch.manual_seed(0  )
  									_lowerCAmelCase       :  List[Any]   				=					ddpm(generator=__snake_case   ,   num_inference_steps=2   ,   output_type="""numpy"""  )[0]
  
  									_lowerCAmelCase       :  Any   				=					image[0, -3:, -3:, -1]
  									_lowerCAmelCase       :  Optional[int]   				=					image_eps[0, -3:, -3:, -1]
  
  									assert image.shape == (1, 3_2, 3_2, 3)
  									_lowerCAmelCase       :  Union[str, 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  ):
  									_lowerCAmelCase       :  Union[str, Any]   				=					"""google/ddpm-cifar10-32"""
  
  									_lowerCAmelCase       :  Optional[int]   				=					UNetaDModel.from_pretrained(__snake_case  )
  									_lowerCAmelCase       :  Tuple   				=					DDPMScheduler.from_pretrained(__snake_case  )
  
  									_lowerCAmelCase       :  str   				=					DDPMPipeline(unet=__snake_case   ,   scheduler=__snake_case  )
  									ddpm.to(__snake_case  )
  									ddpm.set_progress_bar_config(disable=__snake_case  )
  
  									_lowerCAmelCase       :  Any   				=					torch.manual_seed(0  )
  									_lowerCAmelCase       :  Tuple   				=					ddpm(generator=__snake_case   ,   output_type="""numpy"""  ).images
  
  									_lowerCAmelCase       :  int   				=					image[0, -3:, -3:, -1]
  
  									assert image.shape == (1, 3_2, 3_2, 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
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCamelCase_     			=       {
      """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
       UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DeformableDetrForObjectDetection""",
           """DeformableDetrModel""",
           """DeformableDetrPreTrainedModel""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  