





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__     (				__A	):
  __UpperCamelCase						  =	42
  __UpperCamelCase						  =	42






def 				_lowerCAmelCase			(	_a :       int       , _a :       int=0.999       , _a :       Optional[Any]="cosine"       , )						->      List[str]:
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_a :       Any      ):
               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}'      )

     lowerCAmelCase_       :							List[str]			 =		[]
     for i in range(_a      ):
          lowerCAmelCase_       :							Optional[Any]			 =		i / num_diffusion_timesteps
          lowerCAmelCase_       :							Optional[Any]			 =		(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   lowercase__     (				__A  ,       __A	):
  __UpperCamelCase						  =	1







  @register_to_config
  def __init__(						self     ,    _lowercase = 1_000     ,    _lowercase = 0.0001     ,    _lowercase = 0.02     ,    _lowercase = "linear"     ,    _lowercase = 0.0     ,    _lowercase = None     ,    _lowercase = True     ,    ):
       if trained_betas is not None:
            lowerCAmelCase_       :							Any			 =		torch.from_numpy(_lowercase    )
       elif beta_schedule == "linear":
            lowerCAmelCase_       :							List[Any]			 =		torch.linspace(_lowercase     ,    _lowercase     ,    _lowercase     ,    dtype=torch.floataa    )
       elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            lowerCAmelCase_       :							List[str]			 =		(
                torch.linspace(beta_start**0.5     ,    beta_end**0.5     ,    _lowercase     ,    dtype=torch.floataa    ) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            lowerCAmelCase_       :							Dict			 =		betas_for_alpha_bar(_lowercase    )
       elif beta_schedule == "sigmoid":
            # GeoDiff sigmoid schedule
            lowerCAmelCase_       :							Optional[int]			 =		torch.linspace(-6     ,    6     ,    _lowercase    )
            lowerCAmelCase_       :							Any			 =		torch.sigmoid(_lowercase    ) * (beta_end - beta_start) + beta_start
       else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'    )

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

       lowerCAmelCase_       :							str			 =		torch.tensor(1.0    )

       # standard deviation of the initial noise distribution
       lowerCAmelCase_       :							List[str]			 =		1.0

       # setable values
       lowerCAmelCase_       :							Union[str, Any]			 =		None
       lowerCAmelCase_       :							Tuple			 =		torch.from_numpy(np.arange(0     ,    _lowercase    )[::-1].copy()    )

       lowerCAmelCase_       :							List[str]			 =		eta







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase = None    ):
       return sample







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase = 10     ,    _lowercase = 10     ,    _lowercase = None     ,    ):
       lowerCAmelCase_       :							Tuple			 =		min(self.config.num_train_timesteps     ,    _lowercase    )
       lowerCAmelCase_       :							str			 =		num_inference_steps

       lowerCAmelCase_       :							Union[str, Any]			 =		[]

       lowerCAmelCase_       :							Optional[int]			 =		{}
       for j in range(0     ,    num_inference_steps - jump_length     ,    _lowercase    ):
            lowerCAmelCase_       :							Any			 =		jump_n_sample - 1

       lowerCAmelCase_       :							str			 =		num_inference_steps
       while t >= 1:
            lowerCAmelCase_       :							Tuple			 =		t - 1
            timesteps.append(_lowercase    )

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

       lowerCAmelCase_       :							int			 =		np.array(_lowercase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
       lowerCAmelCase_       :							List[Any]			 =		torch.from_numpy(_lowercase    ).to(_lowercase    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							int			 =		t - self.config.num_train_timesteps // self.num_inference_steps

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

       return variance







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase = None     ,    _lowercase = True     ,    ):
       lowerCAmelCase_       :							int			 =		timestep
       lowerCAmelCase_       :							Optional[Any]			 =		timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

       # 6. compute "direction pointing to x_t" of formula (12)
       # from https://arxiv.org/pdf/2010.02502.pdf
       lowerCAmelCase_       :							Dict			 =		(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
       lowerCAmelCase_       :							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
       lowerCAmelCase_       :							List[str]			 =		(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
       lowerCAmelCase_       :							List[str]			 =		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 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase=None    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		self.config.num_train_timesteps // self.num_inference_steps

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

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

       return sample







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    ):
       raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""    )







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







import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
