import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 	A				(							a__	):
   __UpperCAmelCase					:					Dict    =     (CMStochasticIterativeScheduler,)
   __UpperCAmelCase					:					Union[str, Any]    =     10


   def      lowercase_ (self	:		List[str]			,    **__UpperCAmelCase	:		str						)		->							Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=    {
             "num_train_timesteps": 2_0_1,
             "sigma_min": 0.002,
             "sigma_max": 80.0,
         }

         config.update(**lowerCAmelCase__						)
         return config


   def      lowercase_ (self	:		str						)		->							Tuple:
         """simple docstring"""



         UpperCAmelCase__								=    1_0

         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    self.scheduler_classes[0](**lowerCAmelCase__						)

         scheduler.set_timesteps(lowerCAmelCase__						)

         UpperCAmelCase__								=    scheduler.timesteps[0]
         UpperCAmelCase__								=    scheduler.timesteps[1]

         UpperCAmelCase__								=    self.dummy_sample
         UpperCAmelCase__								=    0.1 * sample

         UpperCAmelCase__								=    scheduler.step(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__						).prev_sample
         UpperCAmelCase__								=    scheduler.step(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__						).prev_sample

         self.assertEqual(output_a.shape			,    sample.shape						)
         self.assertEqual(output_a.shape			,    output_a.shape						)


   def      lowercase_ (self	:		int						)		->							str:
         """simple docstring"""



         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)


   def      lowercase_ (self	:		int						)		->							int:
         """simple docstring"""



         for clip_denoised in [True, False]:
               self.check_over_configs(clip_denoised=lowerCAmelCase__						)


   def      lowercase_ (self	:		int						)		->							Dict:
         """simple docstring"""



         UpperCAmelCase__								=    self.scheduler_classes[0]
         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    scheduler_class(**lowerCAmelCase__						)

         UpperCAmelCase__								=    1
         scheduler.set_timesteps(lowerCAmelCase__						)
         UpperCAmelCase__								=    scheduler.timesteps

         UpperCAmelCase__								=    torch.manual_seed(0						)

         UpperCAmelCase__								=    self.dummy_model()
         UpperCAmelCase__								=    self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(lowerCAmelCase__						):
               # 1. scale model input
               UpperCAmelCase__								=    scheduler.scale_model_input(lowerCAmelCase__			,    lowerCAmelCase__						)

               # 2. predict noise residual
               UpperCAmelCase__								=    model(lowerCAmelCase__			,    lowerCAmelCase__						)

               # 3. predict previous sample x_t-1
               UpperCAmelCase__								=    scheduler.step(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    generator=lowerCAmelCase__						).prev_sample

               UpperCAmelCase__								=    pred_prev_sample

         UpperCAmelCase__								=    torch.sum(torch.abs(lowerCAmelCase__						)						)
         UpperCAmelCase__								=    torch.mean(torch.abs(lowerCAmelCase__						)						)

         assert abs(result_sum.item() - 192.7614						) < 1E-2
         assert abs(result_mean.item() - 0.2510						) < 1E-3


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



         UpperCAmelCase__								=    self.scheduler_classes[0]
         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    scheduler_class(**lowerCAmelCase__						)

         UpperCAmelCase__								=    [1_0_6, 0]
         scheduler.set_timesteps(timesteps=lowerCAmelCase__						)
         UpperCAmelCase__								=    scheduler.timesteps

         UpperCAmelCase__								=    torch.manual_seed(0						)

         UpperCAmelCase__								=    self.dummy_model()
         UpperCAmelCase__								=    self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
               # 1. scale model input
               UpperCAmelCase__								=    scheduler.scale_model_input(lowerCAmelCase__			,    lowerCAmelCase__						)

               # 2. predict noise residual
               UpperCAmelCase__								=    model(lowerCAmelCase__			,    lowerCAmelCase__						)

               # 3. predict previous sample x_t-1
               UpperCAmelCase__								=    scheduler.step(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    generator=lowerCAmelCase__						).prev_sample

               UpperCAmelCase__								=    pred_prev_sample

         UpperCAmelCase__								=    torch.sum(torch.abs(lowerCAmelCase__						)						)
         UpperCAmelCase__								=    torch.mean(torch.abs(lowerCAmelCase__						)						)

         assert abs(result_sum.item() - 347.6357						) < 1E-2
         assert abs(result_mean.item() - 0.4527						) < 1E-3


   def      lowercase_ (self	:		int						)		->							Tuple:
         """simple docstring"""



         UpperCAmelCase__								=    self.scheduler_classes[0]
         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    scheduler_class(**lowerCAmelCase__						)

         UpperCAmelCase__								=    [3_9, 3_0, 1_2, 1_5, 0]

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


   def      lowercase_ (self	:		List[str]						)		->							Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=    self.scheduler_classes[0]
         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    scheduler_class(**lowerCAmelCase__						)

         UpperCAmelCase__								=    [3_9, 3_0, 1_2, 1, 0]
         UpperCAmelCase__								=    len(lowerCAmelCase__						)

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





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



         UpperCAmelCase__								=    self.scheduler_classes[0]
         UpperCAmelCase__								=    self.get_scheduler_config()
         UpperCAmelCase__								=    scheduler_class(**lowerCAmelCase__						)

         UpperCAmelCase__								=    [scheduler.config.num_train_timesteps]

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










"""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


