


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Any						      =	(DDPMScheduler,)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				Dict     =	{
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """variance_type""": """fixed_small""",
            """clip_sample""": True,
        }

        config.update(**SCREAMING_SNAKE_CASE_							)
        return config





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        for beta_start, beta_end in zip([0.0001, 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  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        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  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        for t in [0, 500, 999]:
           self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	self.scheduler_classes[0]
        __lowerCamelCase:				Union[str, Any]     =	self.get_scheduler_config()
        __lowerCamelCase:				List[Any]     =	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							) - 0.0_0979							)							) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1E-5





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Optional[int]     =	self.scheduler_classes[0]
        __lowerCamelCase:				int     =	self.get_scheduler_config()
        __lowerCamelCase:				int     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	len(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.dummy_model()
        __lowerCamelCase:				str     =	self.dummy_sample_deter
        __lowerCamelCase:				Tuple     =	torch.manual_seed(0							)

        for t in reversed(range(SCREAMING_SNAKE_CASE_							)							):
           # 1. predict noise residual
           __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # 2. predict previous mean of sample x_t-1
           __lowerCamelCase:				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
           __lowerCamelCase:				Optional[int]     =	pred_prev_sample

        __lowerCamelCase:				Tuple     =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_							)							)
        __lowerCamelCase:				List[Any]     =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_							)							)

        assert abs(result_sum.item() - 258.9606							) < 1E-2
        assert abs(result_mean.item() - 0.3372							) < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Tuple     =	self.scheduler_classes[0]
        __lowerCamelCase:				Dict     =	self.get_scheduler_config(prediction_type="""v_prediction"""							)
        __lowerCamelCase:				int     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.dummy_model()
        __lowerCamelCase:				str     =	self.dummy_sample_deter
        __lowerCamelCase:				Optional[int]     =	torch.manual_seed(0							)

        for t in reversed(range(SCREAMING_SNAKE_CASE_							)							):
           # 1. predict noise residual
           __lowerCamelCase:				Tuple     =	model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # 2. predict previous mean of sample x_t-1
           __lowerCamelCase:				int     =	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
           __lowerCamelCase:				Tuple     =	pred_prev_sample

        __lowerCamelCase:				Tuple     =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_							)							)
        __lowerCamelCase:				Optional[int]     =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_							)							)

        assert abs(result_sum.item() - 202.0296							) < 1E-2
        assert abs(result_mean.item() - 0.2631							) < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[Any]     =	self.scheduler_classes[0]
        __lowerCamelCase:				Tuple     =	self.get_scheduler_config()
        __lowerCamelCase:				List[str]     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	scheduler.timesteps

        for i, timestep in enumerate(SCREAMING_SNAKE_CASE_							):
           if i == len(SCREAMING_SNAKE_CASE_							) - 1:
              __lowerCamelCase:				Union[str, Any]     =	-1
           else:
              __lowerCamelCase:				Any     =	timesteps[i + 1]

           __lowerCamelCase:				Dict     =	scheduler.previous_timestep(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Union[str, Any]     =	prev_t.item()

           self.assertEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Any     =	self.scheduler_classes[0]
        __lowerCamelCase:				str     =	self.get_scheduler_config()
        __lowerCamelCase:				Tuple     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	[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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Tuple     =	self.scheduler_classes[0]
        __lowerCamelCase:				int     =	self.get_scheduler_config()
        __lowerCamelCase:				Optional[Any]     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	[100, 87, 50, 1, 0]
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				int     =	self.scheduler_classes[0]
        __lowerCamelCase:				int     =	self.get_scheduler_config()
        __lowerCamelCase:				List[Any]     =	scheduler_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	[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_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


