

import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       42
 __SCREAMING_SNAKE_CASE      							=       None







def 		UpperCamelCase_(    snake_case__:						Union[str, Any]						,					snake_case__:						List[Any]=0.9_9_9						,					snake_case__:						int="cosine"						,					) ->				List[str]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case__:						Union[str, Any]     ):
   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(snake_case__:						int     ):
   return math.exp(t * -1_2.0     )

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

 UpperCAmelCase__           =       []
 for i in range(snake_case__     ):
  UpperCAmelCase__           =       i / num_diffusion_timesteps
  UpperCAmelCase__           =       (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 					lowercase		( _UpperCamelCase				,       _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       [e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE      							=       1




 @register_to_config
 def __init__(self , __a = 1000 , __a = 0.00_01 , __a = 0.02 , __a = "linear" , __a = None , __a = False , __a = "epsilon" , __a = "linspace" , __a = 0 , )				->      Optional[Any]:






  """simple docstring"""





  if trained_betas is not None:
   UpperCAmelCase__           =       torch.tensor(__a , dtype=torch.floataa				)
  elif beta_schedule == "linear":
   UpperCAmelCase__           =       torch.linspace(__a , __a , __a , dtype=torch.floataa				)
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   UpperCAmelCase__           =       (
       torch.linspace(beta_start**0.5 , beta_end**0.5 , __a , dtype=torch.floataa				) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   UpperCAmelCase__           =       betas_for_alpha_bar(__a				)
  else:
   raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"				)

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

  UpperCAmelCase__           =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
  UpperCAmelCase__           =       np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
  UpperCAmelCase__           =       torch.from_numpy(__a				)

  # setable values
  UpperCAmelCase__           =       None
  UpperCAmelCase__           =       use_karras_sigmas
  self.set_timesteps(__a , __a				)
  UpperCAmelCase__           =       []
  UpperCAmelCase__           =       False




 @property
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  if self.config.timestep_spacing in ["linspace", "trailing"]:
   return self.sigmas.max()

  return (self.sigmas.max() ** 2 + 1) ** 0.5




 def 						UpperCamelCase__							(self , __a , __a				)				->      torch.FloatTensor:






  """simple docstring"""





  if isinstance(__a , torch.Tensor				):
   UpperCAmelCase__           =       timestep.to(self.timesteps.device				)
  UpperCAmelCase__           =       (self.timesteps == timestep).nonzero().item()
  UpperCAmelCase__           =       self.sigmas[step_index]
  UpperCAmelCase__           =       sample / ((sigma**2 + 1) ** 0.5)
  UpperCAmelCase__           =       True
  return sample




 def 						UpperCamelCase__							(self , __a , __a , __a				)				->      int:






  """simple docstring"""





  def lms_derivative(__a				):
   UpperCAmelCase__           =       1.0
   for k in range(__a				):
    if current_order == k:
     continue
    prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
   return prod

  UpperCAmelCase__           =       integrate.quad(__a , self.sigmas[t] , self.sigmas[t + 1] , epsrel=1E-4				)[0]

  return integrated_coeff




 def 						UpperCamelCase__							(self , __a , __a = None				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       num_inference_steps

  # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  if self.config.timestep_spacing == "linspace":
   UpperCAmelCase__           =       np.linspace(0 , self.config.num_train_timesteps - 1 , __a , dtype=__a				)[
       ::-1
   ].copy()
  elif self.config.timestep_spacing == "leading":
   UpperCAmelCase__           =       self.config.num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCAmelCase__           =       (np.arange(0 , __a				) * step_ratio).round()[::-1].copy().astype(__a				)
   timesteps += self.config.steps_offset
  elif self.config.timestep_spacing == "trailing":
   UpperCAmelCase__           =       self.config.num_train_timesteps / self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCAmelCase__           =       (np.arange(self.config.num_train_timesteps , 0 , -step_ratio				)).round().copy().astype(__a				)
   timesteps -= 1
  else:
   raise ValueError(
       F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."				)

  UpperCAmelCase__           =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
  UpperCAmelCase__           =       np.log(__a				)
  UpperCAmelCase__           =       np.interp(__a , np.arange(0 , len(__a				)				) , __a				)

  if self.use_karras_sigmas:
   UpperCAmelCase__           =       self._convert_to_karras(in_sigmas=__a				)
   UpperCAmelCase__           =       np.array([self._sigma_to_t(__a , __a				) for sigma in sigmas]				)

  UpperCAmelCase__           =       np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)

  UpperCAmelCase__           =       torch.from_numpy(__a				).to(device=__a				)
  if str(__a				).startswith('mps'				):
   # mps does not support float64
   UpperCAmelCase__           =       torch.from_numpy(__a				).to(__a , dtype=torch.floataa				)
  else:
   UpperCAmelCase__           =       torch.from_numpy(__a				).to(device=__a				)

  UpperCAmelCase__           =       []




 def 						UpperCamelCase__							(self , __a , __a				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       np.log(__a				)

  # get distribution
  UpperCAmelCase__           =       log_sigma - log_sigmas[:, np.newaxis]

  # get sigmas range
  UpperCAmelCase__           =       np.cumsum((dists >= 0) , axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
  UpperCAmelCase__           =       low_idx + 1

  UpperCAmelCase__           =       log_sigmas[low_idx]
  UpperCAmelCase__           =       log_sigmas[high_idx]

  # interpolate sigmas
  UpperCAmelCase__           =       (low - log_sigma) / (low - high)
  UpperCAmelCase__           =       np.clip(__a , 0 , 1				)

  # transform interpolation to time range
  UpperCAmelCase__           =       (1 - w) * low_idx + w * high_idx
  UpperCAmelCase__           =       t.reshape(sigma.shape				)
  return t




 def 						UpperCamelCase__							(self , __a				)				->      torch.FloatTensor:






  """simple docstring"""





  UpperCAmelCase__           =       in_sigmas[-1].item()
  UpperCAmelCase__           =       in_sigmas[0].item()

  UpperCAmelCase__           =       7.0  # 7.0 is the value used in the paper
  UpperCAmelCase__           =       np.linspace(0 , 1 , self.num_inference_steps				)
  UpperCAmelCase__           =       sigma_min ** (1 / rho)
  UpperCAmelCase__           =       sigma_max ** (1 / rho)
  UpperCAmelCase__           =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  return sigmas




 def 						UpperCamelCase__							(self , __a , __a , __a , __a = 4 , __a = True , )				->      Union[LMSDiscreteSchedulerOutput, Tuple]:






  """simple docstring"""





  if not self.is_scale_input_called:
   warnings.warn(
       'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
       'See `StableDiffusionPipeline` for a usage example.'				)

  if isinstance(__a , torch.Tensor				):
   UpperCAmelCase__           =       timestep.to(self.timesteps.device				)
  UpperCAmelCase__           =       (self.timesteps == timestep).nonzero().item()
  UpperCAmelCase__           =       self.sigmas[step_index]

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  if self.config.prediction_type == "epsilon":
   UpperCAmelCase__           =       sample - sigma * model_output
  elif self.config.prediction_type == "v_prediction":
   # * c_out + input * c_skip
   UpperCAmelCase__           =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  elif self.config.prediction_type == "sample":
   UpperCAmelCase__           =       model_output
  else:
   raise ValueError(
       F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"				)

  # 2. Convert to an ODE derivative
  UpperCAmelCase__           =       (sample - pred_original_sample) / sigma
  self.derivatives.append(__a				)
  if len(self.derivatives				) > order:
   self.derivatives.pop(0				)

  # 3. Compute linear multistep coefficients
  UpperCAmelCase__           =       min(step_index + 1 , __a				)
  UpperCAmelCase__           =       [self.get_lms_coefficient(__a , __a , __a				) for curr_order in range(__a				)]

  # 4. Compute previous sample based on the derivatives path
  UpperCAmelCase__           =       sample + sum(
      coeff * derivative for coeff, derivative in zip(__a , reversed(self.derivatives				)				)				)

  if not return_dict:
   return (prev_sample,)

  return LMSDiscreteSchedulerOutput(prev_sample=__a , pred_original_sample=__a				)




 def 						UpperCamelCase__							(self , __a , __a , __a , )				->      torch.FloatTensor:






  """simple docstring"""





  UpperCAmelCase__           =       self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype				)
  if original_samples.device.type == "mps" and torch.is_floating_point(__a				):
   # mps does not support float64
   UpperCAmelCase__           =       self.timesteps.to(original_samples.device , dtype=torch.floataa				)
   UpperCAmelCase__           =       timesteps.to(original_samples.device , dtype=torch.floataa				)
  else:
   UpperCAmelCase__           =       self.timesteps.to(original_samples.device				)
   UpperCAmelCase__           =       timesteps.to(original_samples.device				)

  UpperCAmelCase__           =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

  UpperCAmelCase__           =       sigmas[step_indices].flatten()
  while len(sigma.shape				) < len(original_samples.shape				):
   UpperCAmelCase__           =       sigma.unsqueeze(-1				)

  UpperCAmelCase__           =       original_samples + noise * sigma
  return noisy_samples




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






  """simple docstring"""





  return self.config.num_train_timesteps






from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 		UpperCamelCase_(    snake_case__:						Optional[int]     ) ->				Tuple:
 if is_torch_available():
  import torch

  if isinstance(snake_case__						,					torch.Tensor     ):
   return True

 return isinstance(snake_case__						,					np.ndarray     )







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       fields(self				)

  # Safety and consistency checks
  if not len(__a				):
   raise ValueError(F"{self.__class__.__name__} has no fields."				)

  UpperCAmelCase__           =       getattr(self , class_fields[0].name				)
  UpperCAmelCase__           =       all(getattr(self , field.name				) is None for field in class_fields[1:]				)

  if other_fields_are_none and isinstance(__a , __a				):
   for key, value in first_field.items():
    UpperCAmelCase__           =       value
  else:
   for field in class_fields:
    UpperCAmelCase__           =       getattr(self , field.name				)
    if v is not None:
     UpperCAmelCase__           =       v




 def __delitem__(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      str:






  """simple docstring"""





  raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Tuple:






  """simple docstring"""





  raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."				)




 def __getitem__(self , __a				)				->      Tuple:






  """simple docstring"""





  if isinstance(__a , __a				):
   UpperCAmelCase__           =       dict(self.items()				)
   return inner_dict[k]
  else:
   return self.to_tuple()[k]




 def __setattr__(self , __a , __a				)				->      Any:






  """simple docstring"""





  if name in self.keys() and value is not None:
   # Don't call self.__setitem__ to avoid recursion errors
   super().__setitem__(__a , __a				)
  super().__setattr__(__a , __a				)




 def __setitem__(self , __a , __a				)				->      Union[str, Any]:






  """simple docstring"""





  super().__setitem__(__a , __a				)
  # Don't call self.__setattr__ to avoid recursion errors
  super().__setattr__(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Tuple[Any]:






  """simple docstring"""





  return tuple(self[k] for k in self.keys()				)



