
'''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




'''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__)


