

'''simple docstring'''


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (DDPMScheduler,)







						def       __magic_name__							(							self					:      Any			,			**snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**snake_case_  )
								return config







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]			,			[0.0_0_2, 0.0_2, 0.2, 2]  ):
										self.check_over_configs(beta_start=snake_case_			,			beta_end=snake_case_  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=snake_case_  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=snake_case_  )







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



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								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       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								for t in [0, 5_0_0, 9_9_9]:
										self.check_over_forward(time_step=snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.scheduler_classes[0]
								snake_case__						:       Optional[Any]       						=  self.get_scheduler_config()
								snake_case__						:       int       						=  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_8_7  ) - 0.0_0_9_7_9  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ) - 0.0_2  )  ) < 1e-5







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



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.scheduler_classes[0]
								snake_case__						:       List[str]       						=  self.get_scheduler_config()
								snake_case__						:       Optional[int]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Optional[Any]       						=  len(snake_case_  )

								snake_case__						:       str       						=  self.dummy_model()
								snake_case__						:       Dict       						=  self.dummy_sample_deter
								snake_case__						:       List[Any]       						=  torch.manual_seed(0  )

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

										# 2. predict previous mean of sample x_t-1
										snake_case__						:       int       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			generator=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
										snake_case__						:       List[str]       						=  pred_prev_sample

								snake_case__						:       List[Any]       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       List[Any]       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 2_5_8.9_6_0_6  ) < 1e-2
								assert abs(result_mean.item() - 0.3_3_7_2  ) < 1e-3







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.scheduler_classes[0]
								snake_case__						:       Optional[Any]       						=  self.get_scheduler_config(prediction_type='''v_prediction'''  )
								snake_case__						:       Any       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Dict       						=  len(snake_case_  )

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

								for t in reversed(range(snake_case_  )  ):
										# 1. predict noise residual
										snake_case__						:       List[Any]       						=  model(snake_case_			,			snake_case_  )

										# 2. predict previous mean of sample x_t-1
										snake_case__						:       Optional[int]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			generator=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
										snake_case__						:       Dict       						=  pred_prev_sample

								snake_case__						:       Tuple       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       Any       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 2_0_2.0_2_9_6  ) < 1e-2
								assert abs(result_mean.item() - 0.2_6_3_1  ) < 1e-3







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.scheduler_classes[0]
								snake_case__						:       List[Any]       						=  self.get_scheduler_config()
								snake_case__						:       List[str]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Dict       						=  [1_0_0, 8_7, 5_0, 1, 0]

								scheduler.set_timesteps(timesteps=snake_case_  )

								snake_case__						:       Optional[int]       						=  scheduler.timesteps

								for i, timestep in enumerate(snake_case_  ):
										if i == len(snake_case_  ) - 1:
												snake_case__						:       Optional[int]       						=  -1
										else:
												snake_case__						:       List[Any]       						=  timesteps[i + 1]

										snake_case__						:       Dict       						=  scheduler.previous_timestep(snake_case_  )
										snake_case__						:       int       						=  prev_t.item()

										self.assertEqual(snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.scheduler_classes[0]
								snake_case__						:       Optional[int]       						=  self.get_scheduler_config()
								snake_case__						:       Tuple       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Dict       						=  [1_0_0, 8_7, 5_0, 5_1, 0]

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







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.scheduler_classes[0]
								snake_case__						:       Optional[int]       						=  self.get_scheduler_config()
								snake_case__						:       Tuple       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Optional[int]       						=  [1_0_0, 8_7, 5_0, 1, 0]
								snake_case__						:       Dict       						=  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       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.scheduler_classes[0]
								snake_case__						:       Tuple       						=  self.get_scheduler_config()
								snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Optional[Any]       						=  [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 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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        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
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



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

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

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







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  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=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



