




"""simple docstring"""





import math
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    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =42
				_lowerCAmelCase						      =42







def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase=0.999   ,      lowerCamelCase="cosine"   ,      ):
						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowerCamelCase		):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowerCamelCase		):
																		return math.exp(t * -12.0		)

						else:
												raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

						snake_case__							:       Dict				      =       []
						for i in range(lowerCamelCase		):
												snake_case__							:       Dict				      =       i / num_diffusion_timesteps
												snake_case__							:       Dict				      =       (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowerCamelCase		) / alpha_bar_fn(lowerCamelCase		)   ,      lowerCamelCase		)		)
						return torch.tensor(lowerCamelCase   ,      dtype=torch.floataa		)

class    __lowercase							(      snake_case_    , snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =1

				@register_to_config
				def __init__( self :  int   ,SCREAMING_SNAKE_CASE_ :  int = 1_0_0_0   ,SCREAMING_SNAKE_CASE_ :  float = 0.0001   ,SCREAMING_SNAKE_CASE_ :  float = 0.02   ,SCREAMING_SNAKE_CASE_ :  str = "linear"   ,SCREAMING_SNAKE_CASE_ :  float = 0.0   ,SCREAMING_SNAKE_CASE_ :  Optional[np.ndarray] = None   ,SCREAMING_SNAKE_CASE_ :  bool = True   ,):
										if trained_betas is not None:
																snake_case__							:       int				      =       torch.from_numpy(SCREAMING_SNAKE_CASE_   )
										elif beta_schedule == "linear":
																snake_case__							:       Union[str, Any]				      =       torch.linspace(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,dtype=torch.floataa   )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																snake_case__							:       Any				      =       (
																    torch.linspace(beta_start**0.5   ,beta_end**0.5   ,SCREAMING_SNAKE_CASE_   ,dtype=torch.floataa   ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																snake_case__							:       str				      =       betas_for_alpha_bar(SCREAMING_SNAKE_CASE_   )
										elif beta_schedule == "sigmoid":
																# GeoDiff sigmoid schedule
																snake_case__							:       Optional[int]				      =       torch.linspace(-6   ,6   ,SCREAMING_SNAKE_CASE_   )
																snake_case__							:       List[str]				      =       torch.sigmoid(SCREAMING_SNAKE_CASE_   ) * (beta_end - beta_start) + beta_start
										else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

										snake_case__							:       Tuple				      =       1.0 - self.betas
										snake_case__							:       str				      =       torch.cumprod(self.alphas   ,dim=0   )
										snake_case__							:       Tuple				      =       torch.tensor(1.0   )

										snake_case__							:       Union[str, Any]				      =       torch.tensor(1.0   )

										# standard deviation of the initial noise distribution
										snake_case__							:       Optional[int]				      =       1.0

										# setable values
										snake_case__							:       List[Any]				      =       None
										snake_case__							:       Union[str, Any]				      =       torch.from_numpy(np.arange(0   ,SCREAMING_SNAKE_CASE_   )[::-1].copy()   )

										snake_case__							:       Any				      =       eta

				def    _lowerCamelCase       ( self :  Optional[Any]   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  Optional[int] = None   ):
										return sample

				def    _lowerCamelCase       ( self :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  int = 1_0   ,SCREAMING_SNAKE_CASE_ :  int = 1_0   ,SCREAMING_SNAKE_CASE_ :  Union[str, torch.device] = None   ,):
										snake_case__							:       Any				      =       min(self.config.num_train_timesteps   ,SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Union[str, Any]				      =       num_inference_steps

										snake_case__							:       str				      =       []

										snake_case__							:       List[Any]				      =       {}
										for j in range(0   ,num_inference_steps - jump_length   ,SCREAMING_SNAKE_CASE_   ):
																snake_case__							:       Union[str, Any]				      =       jump_n_sample - 1

										snake_case__							:       Optional[int]				      =       num_inference_steps
										while t >= 1:
																snake_case__							:       Optional[Any]				      =       t - 1
																timesteps.append(SCREAMING_SNAKE_CASE_   )

																if jumps.get(SCREAMING_SNAKE_CASE_   ,0   ) > 0:
																						snake_case__							:       int				      =       jumps[t] - 1
																						for _ in range(SCREAMING_SNAKE_CASE_   ):
																												snake_case__							:       Any				      =       t + 1
																												timesteps.append(SCREAMING_SNAKE_CASE_   )

										snake_case__							:       Union[str, Any]				      =       np.array(SCREAMING_SNAKE_CASE_   ) * (self.config.num_train_timesteps // self.num_inference_steps)
										snake_case__							:       Optional[int]				      =       torch.from_numpy(SCREAMING_SNAKE_CASE_   ).to(SCREAMING_SNAKE_CASE_   )

				def    _lowerCamelCase       ( self :  Dict   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ):
										snake_case__							:       Tuple				      =       t - self.config.num_train_timesteps // self.num_inference_steps

										snake_case__							:       Dict				      =       self.alphas_cumprod[t]
										snake_case__							:       Union[str, Any]				      =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										snake_case__							:       Optional[Any]				      =       1 - alpha_prod_t
										snake_case__							:       List[Any]				      =       1 - alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from
										# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
										# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
										# variance to pred_sample
										# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
										# without eta.
										# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
										snake_case__							:       List[Any]				      =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance

				def    _lowerCamelCase       ( self :  str   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  Optional[torch.Generator] = None   ,SCREAMING_SNAKE_CASE_ :  bool = True   ,):
										snake_case__							:       Union[str, Any]				      =       timestep
										snake_case__							:       List[str]				      =       timestep - self.config.num_train_timesteps // self.num_inference_steps

										# 1. compute alphas, betas
										snake_case__							:       Optional[Any]				      =       self.alphas_cumprod[t]
										snake_case__							:       Union[str, Any]				      =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										snake_case__							:       Optional[int]				      =       1 - alpha_prod_t

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										snake_case__							:       Optional[int]				      =       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																snake_case__							:       Tuple				      =       torch.clamp(SCREAMING_SNAKE_CASE_   ,-1   ,1   )

										# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
										# substitute formula (7) in the algorithm coming from DDPM paper
										# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
										# DDIM schedule gives the same results as DDPM with eta = 1.0
										# Noise is being reused in 7. and 8., but no impact on quality has
										# been observed.

										# 5. Add noise
										snake_case__							:       Optional[Any]				      =       model_output.device
										snake_case__							:       str				      =       randn_tensor(model_output.shape   ,generator=SCREAMING_SNAKE_CASE_   ,device=SCREAMING_SNAKE_CASE_   ,dtype=model_output.dtype   )
										snake_case__							:       str				      =       self.eta * self._get_variance(SCREAMING_SNAKE_CASE_   ) ** 0.5

										snake_case__							:       Tuple				      =       0
										if t > 0 and self.eta > 0:
																snake_case__							:       Any				      =       std_dev_t * noise

										# 6. compute "direction pointing to x_t" of formula (12)
										# from https://arxiv.org/pdf/2010.02502.pdf
										snake_case__							:       List[str]				      =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

										# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										snake_case__							:       Optional[Any]				      =       alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

										# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
										snake_case__							:       Optional[int]				      =       (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

										# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
										snake_case__							:       Tuple				      =       mask * prev_known_part + (1.0 - mask) * prev_unknown_part

										if not return_dict:
																return (
																    pred_prev_sample,
																    pred_original_sample,
																)

										return RePaintSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_   ,pred_original_sample=SCREAMING_SNAKE_CASE_   )

				def    _lowerCamelCase       ( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  Dict=None   ):
										snake_case__							:       List[Any]				      =       self.config.num_train_timesteps // self.num_inference_steps

										for i in range(SCREAMING_SNAKE_CASE_   ):
																snake_case__							:       int				      =       self.betas[timestep + i]
																if sample.device.type == "mps":
																						# randn does not work reproducibly on mps
																						snake_case__							:       Tuple				      =       randn_tensor(sample.shape   ,dtype=sample.dtype   ,generator=SCREAMING_SNAKE_CASE_   )
																						snake_case__							:       Optional[int]				      =       noise.to(sample.device   )
																else:
																						snake_case__							:       int				      =       randn_tensor(sample.shape   ,generator=SCREAMING_SNAKE_CASE_   ,device=sample.device   ,dtype=sample.dtype   )

																# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																snake_case__							:       List[Any]				      =       (1 - beta) ** 0.5 * sample + beta**0.5 * noise

										return sample

				def    _lowerCamelCase       ( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  torch.FloatTensor   ,SCREAMING_SNAKE_CASE_ :  torch.IntTensor   ,):
										raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''   )





				def __len__( self :  Optional[Any]   ):
										return self.config.num_train_timesteps








"""simple docstring"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


