"""simple docstring"""




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCamelCase__    							= False





class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						str=3_2		)						->    str:
         """simple docstring"""



         set_seed(0		)
         UpperCAmelCase__								=  UNetaDModel(sample_size=__UpperCAmelCase	,  in_channels=3	,  out_channels=3		)
         UpperCAmelCase__								=  torch.optim.SGD(model.parameters()	,  lr=0.0001		)
         return model, optimizer





   @slow
   def       lowercase_				(self					:						Union[str, Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         UpperCAmelCase__								=  DDPMScheduler(
             num_train_timesteps=1_0_0_0	,  beta_start=0.0001	,  beta_end=0.02	,  beta_schedule="linear"	,  clip_sample=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  DDIMScheduler(
             num_train_timesteps=1_0_0_0	,  beta_start=0.0001	,  beta_end=0.02	,  beta_schedule="linear"	,  clip_sample=__UpperCAmelCase	,  )

         assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

         # shared batches for DDPM and DDIM
         set_seed(0		)
         UpperCAmelCase__								=  [torch.randn((4, 3, 3_2, 3_2)		).clip(-1	,  1		).to(__UpperCAmelCase		) for _ in range(4		)]
         UpperCAmelCase__								=  [torch.randn((4, 3, 3_2, 3_2)		).to(__UpperCAmelCase		) for _ in range(4		)]
         UpperCAmelCase__								=  [torch.randint(0	,  1_0_0_0	,  (4,)		).long().to(__UpperCAmelCase		) for _ in range(4		)]

         # train with a DDPM scheduler
         UpperCAmelCase__								=  self.get_model_optimizer(resolution=3_2		)
         model.train().to(__UpperCAmelCase		)
         for i in range(4		):
               optimizer.zero_grad()
               UpperCAmelCase__								=  ddpm_scheduler.add_noise(clean_images[i]	,  noise[i]	,  timesteps[i]		)
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  timesteps[i]		).sample
               UpperCAmelCase__								=  torch.nn.functional.mse_loss(__UpperCAmelCase	,  noise[i]		)
               loss.backward()
               optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         UpperCAmelCase__								=  self.get_model_optimizer(resolution=3_2		)
         model.train().to(__UpperCAmelCase		)
         for i in range(4		):
               optimizer.zero_grad()
               UpperCAmelCase__								=  ddim_scheduler.add_noise(clean_images[i]	,  noise[i]	,  timesteps[i]		)
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  timesteps[i]		).sample
               UpperCAmelCase__								=  torch.nn.functional.mse_loss(__UpperCAmelCase	,  noise[i]		)
               loss.backward()
               optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(__UpperCAmelCase	,  __UpperCAmelCase	,  atol=1E-5		)		)
         self.assertTrue(torch.allclose(__UpperCAmelCase	,  __UpperCAmelCase	,  atol=1E-5		)		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



