





'''simple docstring'''




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   (DDPMParallelScheduler,)



     def      lowerCAmelCase			(   self							:			Any   ,       **__snake_case							:			List[Any]						)->	Optional[Any]:
        snake_case 						=   {
            """num_train_timesteps""": 10_00,
            """beta_start""": 0.00_01,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """variance_type""": """fixed_small""",
            """clip_sample""": True,
        }

        config.update(**__snake_case						)
        return config



     def      lowerCAmelCase			(   self							:			List[str]						)->	Tuple:
        for timesteps in [1, 5, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=__snake_case						)



     def      lowerCAmelCase			(   self							:			int						)->	Union[str, Any]:
        for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,       [0.0_02, 0.02, 0.2, 2]						):
           self.check_over_configs(beta_start=__snake_case   ,       beta_end=__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	str:
        for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=__snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	Dict:
        for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict						)->	Optional[Any]:
        self.check_over_configs(thresholding=__snake_case						)
        for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=__snake_case   ,       prediction_type=__snake_case   ,       sample_max_value=__snake_case   ,       )



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	int:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=__snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]						)->	str:
        for t in [0, 5_00, 9_99]:
           self.check_over_forward(time_step=__snake_case						)



     def      lowerCAmelCase			(   self							:			int						)->	str:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_09_79						)						) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1e-5



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	int:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   len(__snake_case						)

        snake_case 						=   self.dummy_model()
        snake_case 						=   self.dummy_sample_deter
        snake_case 						=   self.dummy_sample_deter + 0.1
        snake_case 						=   self.dummy_sample_deter - 0.1

        snake_case 						=   samplea.shape[0]
        snake_case 						=   torch.stack([samplea, samplea, samplea]   ,       dim=0						)
        snake_case 						=   torch.arange(__snake_case						)[0:3, None].repeat(1   ,       __snake_case						)

        snake_case 						=   model(samples.flatten(0   ,       1						)   ,       timesteps.flatten(0   ,       1						)						)
        snake_case 						=   scheduler.batch_step_no_noise(__snake_case   ,       timesteps.flatten(0   ,       1						)   ,       samples.flatten(0   ,       1						)						)

        snake_case 						=   torch.sum(torch.abs(__snake_case						)						)
        snake_case 						=   torch.mean(torch.abs(__snake_case						)						)

        assert abs(result_sum.item() - 11_53.18_33						) < 1e-2
        assert abs(result_mean.item() - 0.50_05						) < 1e-3



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Any:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   len(__snake_case						)

        snake_case 						=   self.dummy_model()
        snake_case 						=   self.dummy_sample_deter
        snake_case 						=   torch.manual_seed(0						)

        for t in reversed(range(__snake_case						)						):
           # 1. predict noise residual
           snake_case 						=   model(__snake_case   ,       __snake_case						)

           # 2. predict previous mean of sample x_t-1
           snake_case 						=   scheduler.step(__snake_case   ,       __snake_case   ,       __snake_case   ,       generator=__snake_case						).prev_sample

           snake_case 						=   pred_prev_sample

        snake_case 						=   torch.sum(torch.abs(__snake_case						)						)
        snake_case 						=   torch.mean(torch.abs(__snake_case						)						)

        assert abs(result_sum.item() - 2_58.96_06						) < 1e-2
        assert abs(result_mean.item() - 0.33_72						) < 1e-3



     def      lowerCAmelCase			(   self							:			Any						)->	Dict:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config(prediction_type="""v_prediction"""						)
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   len(__snake_case						)

        snake_case 						=   self.dummy_model()
        snake_case 						=   self.dummy_sample_deter
        snake_case 						=   torch.manual_seed(0						)

        for t in reversed(range(__snake_case						)						):
           # 1. predict noise residual
           snake_case 						=   model(__snake_case   ,       __snake_case						)

           # 2. predict previous mean of sample x_t-1
           snake_case 						=   scheduler.step(__snake_case   ,       __snake_case   ,       __snake_case   ,       generator=__snake_case						).prev_sample

           snake_case 						=   pred_prev_sample

        snake_case 						=   torch.sum(torch.abs(__snake_case						)						)
        snake_case 						=   torch.mean(torch.abs(__snake_case						)						)

        assert abs(result_sum.item() - 2_02.02_96						) < 1e-2
        assert abs(result_mean.item() - 0.26_31						) < 1e-3



     def      lowerCAmelCase			(   self							:			Any						)->	Tuple:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   [1_00, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__snake_case						)

        snake_case 						=   scheduler.timesteps

        for i, timestep in enumerate(__snake_case						):
           if i == len(__snake_case						) - 1:
              snake_case 						=   -1
           else:
              snake_case 						=   timesteps[i + 1]

           snake_case 						=   scheduler.previous_timestep(__snake_case						)
           snake_case 						=   prev_t.item()

           self.assertEqual(__snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	int:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   [1_00, 87, 50, 51, 0]

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



     def      lowerCAmelCase			(   self							:			List[Any]						)->	Any:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   [1_00, 87, 50, 1, 0]
        snake_case 						=   len(__snake_case						)

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



     def      lowerCAmelCase			(   self							:			int						)->	List[str]:
        snake_case 						=   self.scheduler_classes[0]
        snake_case 						=   self.get_scheduler_config()
        snake_case 						=   scheduler_class(**__snake_case						)

        snake_case 						=   [scheduler.config.num_train_timesteps]

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








'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

