

'''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      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  torch.FloatTensor
   _SCREAMING_SNAKE_CASE					:  torch.FloatTensor







def       _A	(			snake_case	,							snake_case=0.999	,							snake_case="cosine"	,							)    ->			List[Any]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   _lowercase    :   Dict      =     []
   for i in range(snake_case  ):
      _lowercase    :   Union[str, Any]      =     i / num_diffusion_timesteps
      _lowercase    :   Optional[Any]      =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case  ) / alpha_bar_fn(snake_case  )	,							snake_case  )  )
   return torch.tensor(snake_case	,							dtype=torch.floataa  )


class      a__						(							lowerCamelCase_						,   lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							1


   @register_to_config
   def __init__(				self    ,       _UpperCamelCase = 1000    ,       _UpperCamelCase = 0.0_0_0_1    ,       _UpperCamelCase = 0.0_2    ,       _UpperCamelCase = "linear"    ,       _UpperCamelCase = 0.0    ,       _UpperCamelCase = None    ,       _UpperCamelCase = True    ,       ):





      """simple docstring"""





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

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

      _lowercase    :   str      =     torch.tensor(1.0						)

      # standard deviation of the initial noise distribution
      _lowercase    :   int      =     1.0

      # setable values
      _lowercase    :   int      =     None
      _lowercase    :   Optional[Any]      =     torch.from_numpy(np.arange(0    ,       _UpperCamelCase						)[::-1].copy()						)

      _lowercase    :   Optional[Any]      =     eta


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = None						):





      """simple docstring"""





      return sample


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = 10    ,       _UpperCamelCase = 10    ,       _UpperCamelCase = None    ,       ):





      """simple docstring"""





      _lowercase    :   int      =     min(self.config.num_train_timesteps    ,       _UpperCamelCase						)
      _lowercase    :   Any      =     num_inference_steps

      _lowercase    :   str      =     []

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

      _lowercase    :   List[str]      =     num_inference_steps
      while t >= 1:
         _lowercase    :   Optional[Any]      =     t - 1
         timesteps.append(_UpperCamelCase						)

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

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





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

      _lowercase    :   Optional[Any]      =     self.alphas_cumprod[t]
      _lowercase    :   Tuple      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      _lowercase    :   Tuple      =     1 - alpha_prod_t
      _lowercase    :   Optional[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]
      _lowercase    :   List[Any]      =     (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    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     timestep
      _lowercase    :   str      =     timestep - self.config.num_train_timesteps // self.num_inference_steps

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

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         _lowercase    :   Union[str, Any]      =     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
      _lowercase    :   Dict      =     model_output.device
      _lowercase    :   List[str]      =     randn_tensor(model_output.shape    ,       generator=_UpperCamelCase    ,       device=_UpperCamelCase    ,       dtype=model_output.dtype						)
      _lowercase    :   List[str]      =     self.eta * self._get_variance(_UpperCamelCase						) ** 0.5

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

      # 6. compute "direction pointing to x_t" of formula (12)
      # from https://arxiv.org/pdf/2010.02502.pdf
      _lowercase    :   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
      _lowercase    :   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
      _lowercase    :   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
      _lowercase    :   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=_UpperCamelCase    ,       pred_original_sample=_UpperCamelCase						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase=None						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.config.num_train_timesteps // self.num_inference_steps

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

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

      return sample


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       ):





      """simple docstring"""





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






   def __len__(				self						):





      """simple docstring"""





      return self.config.num_train_timesteps









'''simple docstring'''
def       _A	(			snake_case  )    ->			int:

   # validate:
   if not isinstance(snake_case	,							snake_case  ) or not all(
       isinstance(snake_case	,							snake_case  ) and item >= 0 for item in citations  ):
      raise ValueError("The citations should be a list of non negative integers."  )

   citations.sort()
   _lowercase    :   Tuple      =     len(snake_case  )

   for i in range(snake_case  ):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()






