





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):

      """simple docstring"""


      @property
      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          torch.manual_seed(0			)
          UpperCamelCase__:					List[Any]												=      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:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[int]												=      "cpu"
          UpperCamelCase__:					Dict												=      self.dummy_uncond_unet
          UpperCamelCase__:					Optional[int]												=      DDPMScheduler()

          UpperCamelCase__:					Any												=      DDPMPipeline(unet=__lowerCamelCase					,  scheduler=__lowerCamelCase			)
          ddpm.to(__lowerCamelCase			)
          ddpm.set_progress_bar_config(disable=__lowerCamelCase			)

          UpperCamelCase__:					List[Any]												=      torch.Generator(device=__lowerCamelCase			).manual_seed(0			)
          UpperCamelCase__:					Any												=      ddpm(generator=__lowerCamelCase					,  num_inference_steps=2					,  output_type="numpy"			).images

          UpperCamelCase__:					Optional[int]												=      torch.Generator(device=__lowerCamelCase			).manual_seed(0			)
          UpperCamelCase__:					Optional[Any]												=      ddpm(generator=__lowerCamelCase					,  num_inference_steps=2					,  output_type="numpy"					,  return_dict=__lowerCamelCase			)[0]

          UpperCamelCase__:					Union[str, Any]												=      image[0, -3:, -3:, -1]
          UpperCamelCase__:					Tuple												=      image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCamelCase__:					Union[str, Any]												=      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:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      self.dummy_uncond_unet
          UpperCamelCase__:					Optional[int]												=      DDPMScheduler(prediction_type="sample"			)

          UpperCamelCase__:					Any												=      DDPMPipeline(unet=__lowerCamelCase					,  scheduler=__lowerCamelCase			)
          ddpm.to(__lowerCamelCase			)
          ddpm.set_progress_bar_config(disable=__lowerCamelCase			)

          UpperCamelCase__:					Dict												=      torch.manual_seed(0			)
          UpperCamelCase__:					Dict												=      ddpm(generator=__lowerCamelCase					,  num_inference_steps=2					,  output_type="numpy"			).images

          UpperCamelCase__:					Optional[Any]												=      torch.manual_seed(0			)
          UpperCamelCase__:					Dict												=      ddpm(generator=__lowerCamelCase					,  num_inference_steps=2					,  output_type="numpy"			)[0]

          UpperCamelCase__:					Tuple												=      image[0, -3:, -3:, -1]
          UpperCamelCase__:					List[Any]												=      image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCamelCase__:					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):

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      "google/ddpm-cifar10-32"

          UpperCamelCase__:					Optional[Any]												=      UNetaDModel.from_pretrained(__lowerCamelCase			)
          UpperCamelCase__:					str												=      DDPMScheduler.from_pretrained(__lowerCamelCase			)

          UpperCamelCase__:					str												=      DDPMPipeline(unet=__lowerCamelCase					,  scheduler=__lowerCamelCase			)
          ddpm.to(__lowerCamelCase			)
          ddpm.set_progress_bar_config(disable=__lowerCamelCase			)

          UpperCamelCase__:					str												=      torch.manual_seed(0			)
          UpperCamelCase__:					List[str]												=      ddpm(generator=__lowerCamelCase					,  output_type="numpy"			).images

          UpperCamelCase__:					List[str]												=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCamelCase__:					Optional[Any]												=      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








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

