




"""simple docstring"""


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class _lowerCamelCase				(     UpperCamelCase__   ,       UpperCamelCase__						):
   _lowerCamelCase				:Union[str, Any]	      =	1






   @register_to_config
   def __init__(		self   :		List[Any]    ,	UpperCamelCase   :		Dict=20_00    ,	UpperCamelCase   :		Optional[Any]=0.1    ,	UpperCamelCase   :		Optional[Any]=20    ,	UpperCamelCase   :		Optional[int]=1E-3    ) ->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       None
         lowerCAmelCase__			: List[Any]  					=       None
         lowerCAmelCase__			: Dict  					=       None






   def        _lowerCAmelCase    (		self   :		Union[str, Any]    ,	UpperCamelCase   :		Tuple    ,	UpperCamelCase   :		int = None    ) ->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__			: Union[str, Any]  					=       torch.linspace(1    ,	self.config.sampling_eps    ,	_a    ,	device=_a    )






   def        _lowerCAmelCase    (		self   :		Optional[Any]    ,	UpperCamelCase   :		Union[str, Any]    ,	UpperCamelCase   :		Tuple    ,	UpperCamelCase   :		Union[str, Any]    ,	UpperCamelCase   :		int=None    ) ->						Dict:

         """simple docstring"""
         if self.timesteps is None:
               raise ValueError(
                   """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         lowerCAmelCase__			: Any  					=       (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         lowerCAmelCase__			: List[Any]  					=       torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
         lowerCAmelCase__			: List[str]  					=       std.flatten()
         while len(std.shape    ) < len(score.shape    ):
               lowerCAmelCase__			: List[Any]  					=       std.unsqueeze(-1    )
         lowerCAmelCase__			: int  					=       -score / std

         # compute
         lowerCAmelCase__			: Tuple  					=       -1.0 / len(self.timesteps    )

         lowerCAmelCase__			: str  					=       self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         lowerCAmelCase__			: List[str]  					=       beta_t.flatten()
         while len(beta_t.shape    ) < len(x.shape    ):
               lowerCAmelCase__			: Union[str, Any]  					=       beta_t.unsqueeze(-1    )
         lowerCAmelCase__			: Tuple  					=       -0.5 * beta_t * x

         lowerCAmelCase__			: Tuple  					=       torch.sqrt(_a    )
         lowerCAmelCase__			: Dict  					=       drift - diffusion**2 * score
         lowerCAmelCase__			: Dict  					=       x + drift * dt

         # add noise
         lowerCAmelCase__			: Any  					=       randn_tensor(x.shape    ,	layout=x.layout    ,	generator=_a    ,	device=x.device    ,	dtype=x.dtype    )
         lowerCAmelCase__			: str  					=       x_mean + diffusion * math.sqrt(-dt    ) * noise

         return x, x_mean






   def __len__(		self   :		Dict    ) ->						Optional[Any]:

         """simple docstring"""
         return self.config.num_train_timesteps











import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     str			=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """noise_sampler_seed""": 0,
           }

           config.update(**_a				)
           return config

     def 					a__	(  self				)						->							Tuple:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,     [0.0002, 0.002, 0.02]				):
                 self.check_over_configs(beta_start=_a		,     beta_end=_a				)

     def 					a__	(  self				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps		,     device=_a				)

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

           scheduler.set_timesteps(self.num_inference_steps		,     device=_a				)

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

           for t in scheduler.timesteps:
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

