
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 		__magic_name__				( lowerCAmelCase_					):


  """simple docstring"""





  _UpperCamelCase						 = 42
  _UpperCamelCase						 = None







def 		_lowerCamelCase       (   _a			,   _a=0.999			,   _a="cosine"			,   ):
  """simple docstring"""




  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  _lowerCamelCase      =						[]
  for i in range(_a  ):
    _lowerCamelCase      =						i / num_diffusion_timesteps
    _lowerCamelCase      =						(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 		__magic_name__				( lowerCAmelCase_						,lowerCAmelCase_					):


  """simple docstring"""





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


  @register_to_config
  def __init__(					self	,		a__ = 10_00	,		a__ = 0.0001	,		a__ = 0.02	,		a__ = "linear"	,		a__ = None	,		a__ = False	,		a__ = "epsilon"	,		a__ = "linspace"	,		a__ = 0	,		):
    if trained_betas is not None:
      _lowerCamelCase      =						torch.tensor(__UpperCamelCase	,		dtype=torch.floataa		)
    elif beta_schedule == "linear":
      _lowerCamelCase      =						torch.linspace(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		dtype=torch.floataa		)
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      _lowerCamelCase      =						(
          torch.linspace(beta_start**0.5	,		beta_end**0.5	,		__UpperCamelCase	,		dtype=torch.floataa		) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      _lowerCamelCase      =						betas_for_alpha_bar(__UpperCamelCase		)
    else:
      raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

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

    # setable values
    _lowerCamelCase      =						None
    _lowerCamelCase      =						use_karras_sigmas
    self.set_timesteps(__UpperCamelCase	,		__UpperCamelCase		)
    _lowerCamelCase      =						[]
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    # standard deviation of the initial noise distribution
    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__		):
    if isinstance(__UpperCamelCase	,		torch.Tensor		):
      _lowerCamelCase      =						timestep.to(self.timesteps.device		)
    _lowerCamelCase      =						(self.timesteps == timestep).nonzero().item()
    _lowerCamelCase      =						self.sigmas[step_index]
    _lowerCamelCase      =						sample / ((sigma**2 + 1) ** 0.5)
    _lowerCamelCase      =						True
    return sample


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):

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

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

    return integrated_coeff


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = None		):
    _lowerCamelCase      =						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":
      _lowerCamelCase      =						np.linspace(0	,		self.config.num_train_timesteps - 1	,		__UpperCamelCase	,		dtype=__UpperCamelCase		)[
          ::-1
      ].copy()
    elif self.config.timestep_spacing == "leading":
      _lowerCamelCase      =						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
      _lowerCamelCase      =						(np.arange(0	,		__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase		)
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      _lowerCamelCase      =						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
      _lowerCamelCase      =						(np.arange(self.config.num_train_timesteps	,		0	,		-step_ratio		)).round().copy().astype(__UpperCamelCase		)
      timesteps -= 1
    else:
      raise ValueError(
          f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

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

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

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

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

    _lowerCamelCase      =						[]


  def     _UpperCAmelCase  (					self	,		a__	,		a__		):
    # get log sigma
    _lowerCamelCase      =						np.log(__UpperCamelCase		)

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

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

    _lowerCamelCase      =						log_sigmas[low_idx]
    _lowerCamelCase      =						log_sigmas[high_idx]

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

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


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						in_sigmas[-1].item()
    _lowerCamelCase      =						in_sigmas[0].item()

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


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__ = 4	,		a__ = True	,		):
    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(__UpperCamelCase	,		torch.Tensor		):
      _lowerCamelCase      =						timestep.to(self.timesteps.device		)
    _lowerCamelCase      =						(self.timesteps == timestep).nonzero().item()
    _lowerCamelCase      =						self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      _lowerCamelCase      =						sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      _lowerCamelCase      =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
      _lowerCamelCase      =						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
    _lowerCamelCase      =						(sample - pred_original_sample) / sigma
    self.derivatives.append(__UpperCamelCase		)
    if len(self.derivatives		) > order:
      self.derivatives.pop(0		)

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

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

    if not return_dict:
      return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=__UpperCamelCase	,		pred_original_sample=__UpperCamelCase		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    _lowerCamelCase      =						self.sigmas.to(device=original_samples.device	,		dtype=original_samples.dtype		)
    if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase		):
      # mps does not support float64
      _lowerCamelCase      =						self.timesteps.to(original_samples.device	,		dtype=torch.floataa		)
      _lowerCamelCase      =						timesteps.to(original_samples.device	,		dtype=torch.floataa		)
    else:
      _lowerCamelCase      =						self.timesteps.to(original_samples.device		)
      _lowerCamelCase      =						timesteps.to(original_samples.device		)

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

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

    _lowerCamelCase      =						original_samples + noise * sigma
    return noisy_samples


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






"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



