



"""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 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		torch.FloatTensor
	_SCREAMING_SNAKE_CASE  :		torch.FloatTensor






def    __snake_case       (  __A			:	str       ,		__A			:	Any=0.9_9_9       ,		__A			:	str="cosine"       ,		)    ->					Dict:






				'''simple docstring'''
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__A			:	int    ):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__A			:	Union[str, Any]    ):
												return math.exp(t * -12.0    )

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

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







class 						lowerCAmelCase__	(  _lowerCamelCase       ,       _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   1





	@register_to_config
	def __init__(			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					int = 1_000    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_0_0_1    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_2    ,					_SCREAMING_SNAKE_CASE							:					str = "linear"    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0    ,					_SCREAMING_SNAKE_CASE							:					Optional[np.ndarray] = None    ,					_SCREAMING_SNAKE_CASE							:					bool = True    ,					)	->  int:





					"""simple docstring"""


					if trained_betas is not None:
									SCREAMING_SNAKE_CASE	:       int 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						)
					elif beta_schedule == "linear":
									SCREAMING_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.
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (
									    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
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    betas_for_alpha_bar(_SCREAMING_SNAKE_CASE						)
					elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									SCREAMING_SNAKE_CASE	:       str 							=    torch.linspace(-6    ,					6    ,					_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.sigmoid(_SCREAMING_SNAKE_CASE						) * (beta_end - beta_start) + beta_start
					else:
									raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    1.0 - self.betas
					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.cumprod(self.alphas    ,					dim=0						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.tensor(1.0						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.tensor(1.0						)

					# standard deviation of the initial noise distribution
					SCREAMING_SNAKE_CASE	:       Any 							=    1.0

					# setable values
					SCREAMING_SNAKE_CASE	:       str 							=    None
					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.from_numpy(np.arange(0    ,					_SCREAMING_SNAKE_CASE						)[::-1].copy()						)

					SCREAMING_SNAKE_CASE	:       str 							=    eta





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None						)	->  torch.FloatTensor:





					"""simple docstring"""


					return sample





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					int = 10    ,					_SCREAMING_SNAKE_CASE							:					int = 10    ,					_SCREAMING_SNAKE_CASE							:					Union[str, torch.device] = None    ,					)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    min(self.config.num_train_timesteps    ,					_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    num_inference_steps

					SCREAMING_SNAKE_CASE	:       str 							=    []

					SCREAMING_SNAKE_CASE	:       Tuple 							=    {}
					for j in range(0    ,					num_inference_steps - jump_length    ,					_SCREAMING_SNAKE_CASE						):
									SCREAMING_SNAKE_CASE	:       Tuple 							=    jump_n_sample - 1

					SCREAMING_SNAKE_CASE	:       Dict 							=    num_inference_steps
					while t >= 1:
									SCREAMING_SNAKE_CASE	:       Dict 							=    t - 1
									timesteps.append(_SCREAMING_SNAKE_CASE						)

									if jumps.get(_SCREAMING_SNAKE_CASE    ,					0						) > 0:
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    jumps[t] - 1
													for _ in range(_SCREAMING_SNAKE_CASE						):
																	SCREAMING_SNAKE_CASE	:       str 							=    t + 1
																	timesteps.append(_SCREAMING_SNAKE_CASE						)

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





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    t - self.config.num_train_timesteps // self.num_inference_steps

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					SCREAMING_SNAKE_CASE	:       int 							=    1 - alpha_prod_t
					SCREAMING_SNAKE_CASE	:       Union[str, 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]
					SCREAMING_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    ,					)	->  Union[RePaintSchedulerOutput, Tuple]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    timestep
					SCREAMING_SNAKE_CASE	:       str 							=    timestep - self.config.num_train_timesteps // self.num_inference_steps

					# 1. compute alphas, betas
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    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
					SCREAMING_SNAKE_CASE	:       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:
									SCREAMING_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
					SCREAMING_SNAKE_CASE	:       int 							=    model_output.device
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    randn_tensor(model_output.shape    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=_SCREAMING_SNAKE_CASE    ,					dtype=model_output.dtype						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.eta * self._get_variance(_SCREAMING_SNAKE_CASE						) ** 0.5

					SCREAMING_SNAKE_CASE	:       int 							=    0
					if t > 0 and self.eta > 0:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    std_dev_t * noise

					# 6. compute "direction pointing to x_t" of formula (12)
					# from https://arxiv.org/pdf/2010.02502.pdf
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    (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
					SCREAMING_SNAKE_CASE	:       int 							=    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
					SCREAMING_SNAKE_CASE	:       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
					SCREAMING_SNAKE_CASE	:       Optional[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=_SCREAMING_SNAKE_CASE    ,					pred_original_sample=_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.config.num_train_timesteps // self.num_inference_steps

					for i in range(_SCREAMING_SNAKE_CASE						):
									SCREAMING_SNAKE_CASE	:       int 							=    self.betas[timestep + i]
									if sample.device.type == "mps":
													# randn does not work reproducibly on mps
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    randn_tensor(sample.shape    ,					dtype=sample.dtype    ,					generator=_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    noise.to(sample.device						)
									else:
													SCREAMING_SNAKE_CASE	:       Dict 							=    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
									SCREAMING_SNAKE_CASE	:       str 							=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

					return sample





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.IntTensor    ,					)	->  torch.FloatTensor:





					"""simple docstring"""


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







	def __len__(			self							:					Optional[int]						)	->  List[str]:





					"""simple docstring"""


					return self.config.num_train_timesteps










"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  None:





					"""simple docstring"""


					warnings.warn(
					    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use ConvNextImageProcessor instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





