


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					(DDPMScheduler,)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		{
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''variance_type''': '''fixed_small''',
                  '''clip_sample''': True,
              }

              config.update(**__SCREAMING_SNAKE_CASE						)
              return config






       def  _lowercase   (     self						):




              """simple docstring"""




              for timesteps in [1, 5, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,					[0.002, 0.02, 0.2, 2]						):
                     self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE ,					beta_end=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              for variance in ["fixed_small", "fixed_large", "other"]:
                     self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE						)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__SCREAMING_SNAKE_CASE ,					prediction_type=__SCREAMING_SNAKE_CASE ,					sample_max_value=__SCREAMING_SNAKE_CASE ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                     self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              for t in [0, 500, 999]:
                     self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		self.scheduler_classes[0]
              UpperCamelCase    :	Union[str, Any]   	=		self.get_scheduler_config()
              UpperCamelCase    :	Optional[int]   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.00_979						)						) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.02						)						) < 1e-5






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.scheduler_classes[0]
              UpperCamelCase    :	int   	=		self.get_scheduler_config()
              UpperCamelCase    :	Optional[Any]   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		len(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		self.dummy_model()
              UpperCamelCase    :	str   	=		self.dummy_sample_deter
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)

              for t in reversed(range(__SCREAMING_SNAKE_CASE						)						):
                     # 1. predict noise residual
                     UpperCamelCase    :	List[Any]   	=		model(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                     # 2. predict previous mean of sample x_t-1
                     UpperCamelCase    :	List[str]   	=		scheduler.step(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE						).prev_sample

                     # if t > 0:
                     #     noise = self.dummy_sample_deter
                     #     variance = scheduler.get_variance(t) ** (0.5) * noise
                     #
                     # sample = pred_prev_sample + variance
                     UpperCamelCase    :	Optional[int]   	=		pred_prev_sample

              UpperCamelCase    :	Tuple   	=		torch.sum(torch.abs(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	List[str]   	=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

              assert abs(result_sum.item() - 258.9_606						) < 1e-2
              assert abs(result_mean.item() - 0.3_372						) < 1e-3






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.scheduler_classes[0]
              UpperCamelCase    :	List[str]   	=		self.get_scheduler_config(prediction_type='''v_prediction'''						)
              UpperCamelCase    :	Optional[int]   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		len(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		self.dummy_model()
              UpperCamelCase    :	str   	=		self.dummy_sample_deter
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)

              for t in reversed(range(__SCREAMING_SNAKE_CASE						)						):
                     # 1. predict noise residual
                     UpperCamelCase    :	List[Any]   	=		model(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                     # 2. predict previous mean of sample x_t-1
                     UpperCamelCase    :	str   	=		scheduler.step(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE						).prev_sample

                     # if t > 0:
                     #     noise = self.dummy_sample_deter
                     #     variance = scheduler.get_variance(t) ** (0.5) * noise
                     #
                     # sample = pred_prev_sample + variance
                     UpperCamelCase    :	Optional[int]   	=		pred_prev_sample

              UpperCamelCase    :	Dict   	=		torch.sum(torch.abs(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Union[str, Any]   	=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE						)						)

              assert abs(result_sum.item() - 202.0_296						) < 1e-2
              assert abs(result_mean.item() - 0.2_631						) < 1e-3






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.scheduler_classes[0]
              UpperCamelCase    :	Dict   	=		self.get_scheduler_config()
              UpperCamelCase    :	Any   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		[100, 87, 50, 1, 0]

              scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		scheduler.timesteps

              for i, timestep in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == len(__SCREAMING_SNAKE_CASE						) - 1:
                            UpperCamelCase    :	int   	=		-1
                     else:
                            UpperCamelCase    :	List[Any]   	=		timesteps[i + 1]

                     UpperCamelCase    :	int   	=		scheduler.previous_timestep(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Dict   	=		prev_t.item()

                     self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.scheduler_classes[0]
              UpperCamelCase    :	Optional[Any]   	=		self.get_scheduler_config()
              UpperCamelCase    :	List[str]   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		[100, 87, 50, 51, 0]

              with self.assertRaises(__SCREAMING_SNAKE_CASE ,					msg='''`custom_timesteps` must be in descending order.'''						):
                     scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.scheduler_classes[0]
              UpperCamelCase    :	Tuple   	=		self.get_scheduler_config()
              UpperCamelCase    :	Optional[Any]   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		[100, 87, 50, 1, 0]
              UpperCamelCase    :	Any   	=		len(__SCREAMING_SNAKE_CASE						)

              with self.assertRaises(__SCREAMING_SNAKE_CASE ,					msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''						):
                     scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE ,					timesteps=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.scheduler_classes[0]
              UpperCamelCase    :	Tuple   	=		self.get_scheduler_config()
              UpperCamelCase    :	Any   	=		scheduler_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		[scheduler.config.num_train_timesteps]

              with self.assertRaises(
                  __SCREAMING_SNAKE_CASE ,					msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,					):
                     scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE						)






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,
  )

