






'''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 					__UpperCAmelCase (		_SCREAMING_SNAKE_CASE	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					4_2
      SCREAMING_SNAKE_CASE				:    int				       =					4_2






def  lowerCAmelCase__		(					a_  :     Optional[int]			,						a_  :     List[str]=0.999			,						a_  :     Any="cosine"			,						)   -> Optional[Any]:

       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

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




class 					__UpperCAmelCase (		_SCREAMING_SNAKE_CASE      ,				_SCREAMING_SNAKE_CASE	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					1




      @register_to_config
      def __init__(					self  ,		_UpperCAmelCase = 1000  ,		_UpperCAmelCase = 0.00_01  ,		_UpperCAmelCase = 0.02  ,		_UpperCAmelCase = "linear"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = True  ,		):





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

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

             UpperCAmelCase__	:   Any												=	torch.tensor(1.0						)

             # standard deviation of the initial noise distribution
             UpperCAmelCase__	:   Union[str, Any]												=	1.0

             # setable values
             UpperCAmelCase__	:   int												=	None
             UpperCAmelCase__	:   List[str]												=	torch.from_numpy(np.arange(0  ,		_UpperCAmelCase						)[::-1].copy()						)

             UpperCAmelCase__	:   str												=	eta




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None						):
             return sample




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 10  ,		_UpperCAmelCase = 10  ,		_UpperCAmelCase = None  ,		):
             UpperCAmelCase__	:   Union[str, Any]												=	min(self.config.num_train_timesteps  ,		_UpperCAmelCase						)
             UpperCAmelCase__	:   Any												=	num_inference_steps

             UpperCAmelCase__	:   Tuple												=	[]

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

             UpperCAmelCase__	:   List[str]												=	num_inference_steps
             while t >= 1:
                    UpperCAmelCase__	:   Union[str, Any]												=	t - 1
                    timesteps.append(_UpperCAmelCase						)

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

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




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	t - self.config.num_train_timesteps // self.num_inference_steps

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

             return variance




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = True  ,		):
             UpperCAmelCase__	:   Optional[Any]												=	timestep
             UpperCAmelCase__	:   Dict												=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

             # 6. compute "direction pointing to x_t" of formula (12)
             # from https://arxiv.org/pdf/2010.02502.pdf
             UpperCAmelCase__	:   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
             UpperCAmelCase__	:   List[str]												=	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
             UpperCAmelCase__	:   Optional[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
             UpperCAmelCase__	:   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=_UpperCAmelCase  ,		pred_original_sample=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None						):
             UpperCAmelCase__	:   Tuple												=	self.config.num_train_timesteps // self.num_inference_steps

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

                    # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                    UpperCAmelCase__	:   int												=	(1 - beta) ** 0.5 * sample + beta**0.5 * noise

             return sample




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''						)




      def __len__(					self						):
             return self.config.num_train_timesteps

'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


A__ :		Optional[int]					=logging.get_logger(__name__)







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Union[str, Any]					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   warnings.warn(
       """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use ViTImageProcessor instead."""					, lowerCamelCase					, )
   super().__init__(*lowerCamelCase					, **lowerCamelCase							)



