
"""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  _snake_case	(    SCREAMING_SNAKE_CASE_							):
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    42






def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_=0.999						,					lowerCamelCase_="cosine"						,					)  ->      Optional[Any]:


					'''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}"""			)

					__a:    int		     =       []
					for i in range(lowercase_			):
										__a:    int		     =       i / num_diffusion_timesteps
										__a:    List[str]		     =       (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(lowercase_			) / alpha_bar_fn(lowercase_			)						,					lowercase_			)			)
					return torch.tensor(lowercase_						,					dtype=torch.floataa			)




class  _snake_case	(    SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_							):
							SCREAMING_SNAKE_CASE		  =    1






							@register_to_config
							def __init__(       self    :      str    ,			snake_case__    :      int = 1_0_0_0    ,			snake_case__    :      float = 0.0_001    ,			snake_case__    :      float = 0.02    ,			snake_case__    :      str = "linear"    ,			snake_case__    :      float = 0.0    ,			snake_case__    :      Optional[np.ndarray] = None    ,			snake_case__    :      bool = True    ,			):






												"""simple docstring"""



												if trained_betas is not None:
																	__a:    Tuple		     =       torch.from_numpy(lowercase_     )
												elif beta_schedule == "linear":
																	__a:    str		     =       torch.linspace(lowercase_    ,			lowercase_    ,			lowercase_    ,			dtype=torch.floataa     )
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	__a:    Dict		     =       (
																	    torch.linspace(beta_start**0.5    ,			beta_end**0.5    ,			lowercase_    ,			dtype=torch.floataa     ) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	__a:    Any		     =       betas_for_alpha_bar(lowercase_     )
												elif beta_schedule == "sigmoid":
																	# GeoDiff sigmoid schedule
																	__a:    Optional[Any]		     =       torch.linspace(-6    ,			6    ,			lowercase_     )
																	__a:    Optional[int]		     =       torch.sigmoid(lowercase_     ) * (beta_end - beta_start) + beta_start
												else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

												__a:    List[str]		     =       1.0 - self.betas
												__a:    str		     =       torch.cumprod(self.alphas    ,			dim=0     )
												__a:    Union[str, Any]		     =       torch.tensor(1.0     )

												__a:    Any		     =       torch.tensor(1.0     )

												# standard deviation of the initial noise distribution
												__a:    Optional[Any]		     =       1.0

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

												__a:    List[Any]		     =       eta






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






												"""simple docstring"""



												return sample






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      int    ,			snake_case__    :      int = 1_0    ,			snake_case__    :      int = 1_0    ,			snake_case__    :      Union[str, torch.device] = None    ,			):






												"""simple docstring"""



												__a:    Any		     =       min(self.config.num_train_timesteps    ,			lowercase_     )
												__a:    Dict		     =       num_inference_steps

												__a:    List[Any]		     =       []

												__a:    List[str]		     =       {}
												for j in range(0    ,			num_inference_steps - jump_length    ,			lowercase_     ):
																	__a:    Dict		     =       jump_n_sample - 1

												__a:    Dict		     =       num_inference_steps
												while t >= 1:
																	__a:    Optional[int]		     =       t - 1
																	timesteps.append(lowercase_     )

																	if jumps.get(lowercase_    ,			0     ) > 0:
																						__a:    List[str]		     =       jumps[t] - 1
																						for _ in range(lowercase_     ):
																											__a:    List[str]		     =       t + 1
																											timesteps.append(lowercase_     )

												__a:    Union[str, Any]		     =       np.array(lowercase_     ) * (self.config.num_train_timesteps // self.num_inference_steps)
												__a:    Tuple		     =       torch.from_numpy(lowercase_     ).to(lowercase_     )






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    List[str]		     =       t - self.config.num_train_timesteps // self.num_inference_steps

												__a:    Optional[int]		     =       self.alphas_cumprod[t]
												__a:    Optional[Any]		     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												__a:    Optional[Any]		     =       1 - alpha_prod_t
												__a:    List[str]		     =       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]
												__a:    str		     =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      int    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      Optional[torch.Generator] = None    ,			snake_case__    :      bool = True    ,			):






												"""simple docstring"""



												__a:    Any		     =       timestep
												__a:    Optional[Any]		     =       timestep - self.config.num_train_timesteps // self.num_inference_steps

												# 1. compute alphas, betas
												__a:    Optional[Any]		     =       self.alphas_cumprod[t]
												__a:    Optional[Any]		     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												__a:    Dict		     =       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
												__a:    List[str]		     =       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																	__a:    Dict		     =       torch.clamp(lowercase_    ,			-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
												__a:    List[Any]		     =       model_output.device
												__a:    Tuple		     =       randn_tensor(model_output.shape    ,			generator=lowercase_    ,			device=lowercase_    ,			dtype=model_output.dtype     )
												__a:    Optional[Any]		     =       self.eta * self._get_variance(lowercase_     ) ** 0.5

												__a:    Any		     =       0
												if t > 0 and self.eta > 0:
																	__a:    List[str]		     =       std_dev_t * noise

												# 6. compute "direction pointing to x_t" of formula (12)
												# from https://arxiv.org/pdf/2010.02502.pdf
												__a:    Tuple		     =       (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
												__a:    Union[str, 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
												__a:    List[Any]		     =       (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
												__a:    Any		     =       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=lowercase_    ,			pred_original_sample=lowercase_     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      Tuple    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Dict=None     ):






												"""simple docstring"""



												__a:    List[str]		     =       self.config.num_train_timesteps // self.num_inference_steps

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

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

												return sample






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      torch.FloatTensor    ,			snake_case__    :      torch.IntTensor    ,			):






												"""simple docstring"""



												raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""     )






							def __len__(       self    :      List[Any]     ):






												"""simple docstring"""



												return self.config.num_train_timesteps





"""simple docstring"""


def 					_lowerCAmelCase (  lowercase_		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





