






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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



@dataclass
class 				__UpperCAmelCase		(_lowerCAmelCase   ):
   __snake_case    :				torch.FloatTensor
   __snake_case    :				torch.FloatTensor
   __snake_case    :				Optional[torch.FloatTensor]									=							None



class 				__UpperCAmelCase		(_lowerCAmelCase	,_lowerCAmelCase   ):
   __snake_case    :				int									=							2







   @register_to_config
   def __init__(     self:  Any				,     UpperCAmelCase_:  float = 0.02				,     UpperCAmelCase_:  float = 100				,     UpperCAmelCase_:  float = 1.0_07				,     UpperCAmelCase_:  float = 80				,     UpperCAmelCase_:  float = 0.05				,     UpperCAmelCase_:  float = 50				,     ):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  sigma_max

         # setable values
         _SCREAMING_SNAKE_CASE     				=  None
         _SCREAMING_SNAKE_CASE     				=  None
         _SCREAMING_SNAKE_CASE     				=  None  # sigma(t_i)







   def 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  Optional[int] = None					):







         '''simple docstring'''




         return sample







   def 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Union[str, torch.device] = None					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  num_inference_steps
         _SCREAMING_SNAKE_CASE     				=  np.arange(0				,     self.num_inference_steps					)[::-1].copy()
         _SCREAMING_SNAKE_CASE     				=  torch.from_numpy(_lowercase					).to(_lowercase					)
         _SCREAMING_SNAKE_CASE     				=  [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps
         ]
         _SCREAMING_SNAKE_CASE     				=  torch.tensor(_lowercase				,     dtype=torch.floataa				,     device=_lowercase					)







   def 							UpperCamelCase				(     self:  str				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  float				,     UpperCAmelCase_:  Optional[torch.Generator] = None					):







         '''simple docstring'''




         if self.config.s_min <= sigma <= self.config.s_max:
               _SCREAMING_SNAKE_CASE     				=  min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1					)
         else:
               _SCREAMING_SNAKE_CASE     				=  0

         # sample eps ~ N(0, S_noise^2 * I)
         _SCREAMING_SNAKE_CASE     				=  self.config.s_noise * randn_tensor(sample.shape				,     generator=_lowercase					).to(sample.device					)
         _SCREAMING_SNAKE_CASE     				=  sigma + gamma * sigma
         _SCREAMING_SNAKE_CASE     				=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat







   def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  float				,     UpperCAmelCase_:  float				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  bool = True				,     ):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  sample_hat + sigma_hat * model_output
         _SCREAMING_SNAKE_CASE     				=  (sample_hat - pred_original_sample) / sigma_hat
         _SCREAMING_SNAKE_CASE     				=  sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
               return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=_lowercase				,     derivative=_lowercase				,     pred_original_sample=_lowercase					)







   def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  float				,     UpperCAmelCase_:  float				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  bool = True				,     ):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  sample_prev + sigma_prev * model_output
         _SCREAMING_SNAKE_CASE     				=  (sample_prev - pred_original_sample) / sigma_prev
         _SCREAMING_SNAKE_CASE     				=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
               return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=_lowercase				,     derivative=_lowercase				,     pred_original_sample=_lowercase					)







   def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Tuple					):







         '''simple docstring'''




         raise NotImplementedError()









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

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

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

