



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 (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     42
 lowerCamelCase_       =     42







def       UpperCAmelCase__	(							__snake_case  ,			__snake_case=0.9_99  ,			__snake_case="cosine"  ,			)				->	List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__snake_case							):
      return math.cos((t + 0.0_08) / 1.0_08 * 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}'''							)

  _A          =      []
  for i in range(__snake_case							):
    _A          =      i / num_diffusion_timesteps
    _A          =      (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 						_snake_case (     lowerCamelCase   ,lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     1
 @register_to_config
 def __init__(  self		,      a = 1_0_0_0		,      a = 0.0_001		,      a = 0.02		,      a = "linear"		,      a = 0.0		,      a = None		,      a = True		,      )		->							Optional[Any]:




   """simple docstring"""







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

   _A          =      1.0 - self.betas
   _A          =      torch.cumprod(self.alphas		,      dim=0   )
   _A          =      torch.tensor(1.0   )

   _A          =      torch.tensor(1.0   )

   # standard deviation of the initial noise distribution
   _A          =      1.0

   # setable values
   _A          =      None
   _A          =      torch.from_numpy(np.arange(0		,      a   )[::-1].copy()   )

   _A          =      eta
 def lowercase_						(  self		,      a		,      a = None   )		->							torch.FloatTensor:




   """simple docstring"""







   return sample
 def lowercase_						(  self		,      a		,      a = 1_0		,      a = 1_0		,      a = None		,      )		->							List[str]:




   """simple docstring"""







   _A          =      min(self.config.num_train_timesteps		,      a   )
   _A          =      num_inference_steps

   _A          =      []

   _A          =      {}
   for j in range(0		,      num_inference_steps - jump_length		,      a   ):
     _A          =      jump_n_sample - 1

   _A          =      num_inference_steps
   while t >= 1:
     _A          =      t - 1
     timesteps.append(a   )

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

   _A          =      np.array(a   ) * (self.config.num_train_timesteps // self.num_inference_steps)
   _A          =      torch.from_numpy(a   ).to(a   )
 def lowercase_						(  self		,      a   )		->							str:




   """simple docstring"""







   _A          =      t - self.config.num_train_timesteps // self.num_inference_steps

   _A          =      self.alphas_cumprod[t]
   _A          =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
   _A          =      1 - alpha_prod_t
   _A          =      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          =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

   return variance
 def lowercase_						(  self		,      a		,      a		,      a		,      a		,      a		,      a = None		,      a = True		,      )		->							Union[RePaintSchedulerOutput, Tuple]:




   """simple docstring"""







   _A          =      timestep
   _A          =      timestep - self.config.num_train_timesteps // self.num_inference_steps

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

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     _A          =      torch.clamp(a		,      -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          =      model_output.device
   _A          =      randn_tensor(model_output.shape		,      generator=a		,      device=a		,      dtype=model_output.dtype   )
   _A          =      self.eta * self._get_variance(a   ) ** 0.5

   _A          =      0
   if t > 0 and self.eta > 0:
     _A          =      std_dev_t * noise

   # 6. compute "direction pointing to x_t" of formula (12)
   # from https://arxiv.org/pdf/2010.02502.pdf
   _A          =      (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          =      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          =      (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          =      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=a		,      pred_original_sample=a   )
 def lowercase_						(  self		,      a		,      a		,      a=None   )		->							Tuple:




   """simple docstring"""







   _A          =      self.config.num_train_timesteps // self.num_inference_steps

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

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

   return sample
 def lowercase_						(  self		,      a		,      a		,      a		,      )		->							torch.FloatTensor:




   """simple docstring"""







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



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




   """simple docstring"""







   return self.config.num_train_timesteps




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)