


'''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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: torch.FloatTensor
 __lowerCAmelCase						: Optional[torch.FloatTensor]							  =		None





def    UpperCAmelCase_  (       A		,	A=0.9_99		,	A="cosine"		,	):
   '''simple docstring'''





   if alpha_transform_type == "cosine":

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

   _a					:  int						=  []
   for i in range(A      ):
      _a					:  Optional[Any]						=  i / num_diffusion_timesteps
      _a					:  List[str]						=  (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 					a			(				snake_case__			,							snake_case__  ):
 '''simple docstring'''




 @register_to_config
 def __init__(    self		,      lowerCamelCase_ = 1_0_0_0		,      lowerCamelCase_ = "fixed_small_log"		,      lowerCamelCase_ = True		,      lowerCamelCase_ = 1.0		,      lowerCamelCase_ = "epsilon"		,      lowerCamelCase_ = "squaredcos_cap_v2"		,      )      ->    str:
    if beta_schedule != "squaredcos_cap_v2":
       raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''      )

    _a					:  Tuple						=  betas_for_alpha_bar(lowerCamelCase_      )

    _a					:  List[Any]						=  1.0 - self.betas
    _a					:  Optional[Any]						=  torch.cumprod(self.alphas		,      dim=0      )
    _a					:  Dict						=  torch.tensor(1.0      )

    # standard deviation of the initial noise distribution
    _a					:  Union[str, Any]						=  1.0

    # setable values
    _a					:  Dict						=  None
    _a					:  Any						=  torch.from_numpy(np.arange(0		,      lowerCamelCase_      )[::-1].copy()      )

    _a					:  Tuple						=  variance_type




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    torch.FloatTensor:
    return sample




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[Any]:
    _a					:  Union[str, Any]						=  num_inference_steps
    _a					:  Any						=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
    _a					:  str						=  (np.arange(0		,      lowerCamelCase_      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
    _a					:  str						=  torch.from_numpy(lowerCamelCase_      ).to(lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None      )      ->    Dict:
    if prev_timestep is None:
       _a					:  Dict						=  t - 1

    _a					:  Union[str, Any]						=  self.alphas_cumprod[t]
    _a					:  Optional[Any]						=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    _a					:  Any						=  1 - alpha_prod_t
    _a					:  Optional[int]						=  1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       _a					:  int						=  self.betas[t]
    else:
       _a					:  Union[str, Any]						=  1 - alpha_prod_t / 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
    _a					:  Any						=  beta_prod_t_prev / beta_prod_t * beta

    if variance_type is None:
       _a					:  Optional[int]						=  self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small_log":
       _a					:  List[Any]						=  torch.log(torch.clamp(lowerCamelCase_		,      min=1e-20      )      )
       _a					:  int						=  torch.exp(0.5 * variance      )
    elif variance_type == "learned_range":
       # NOTE difference with DDPM scheduler
       _a					:  int						=  variance.log()
       _a					:  Optional[Any]						=  beta.log()

       _a					:  Optional[Any]						=  (predicted_variance + 1) / 2
       _a					:  Optional[Any]						=  frac * max_log + (1 - frac) * min_log

    return variance




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_=None		,      lowerCamelCase_ = True		,      )      ->    Union[UnCLIPSchedulerOutput, Tuple]:
    _a					:  List[str]						=  timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
       _a		,						_a					:  List[Any]						=  torch.split(lowerCamelCase_		,      sample.shape[1]		,      dim=1      )
    else:
       _a					:  Optional[int]						=  None

    # 1. compute alphas, betas
    if prev_timestep is None:
       _a					:  List[str]						=  t - 1

    _a					:  Optional[int]						=  self.alphas_cumprod[t]
    _a					:  List[Any]						=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    _a					:  str						=  1 - alpha_prod_t
    _a					:  Optional[Any]						=  1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       _a					:  List[str]						=  self.betas[t]
       _a					:  List[str]						=  self.alphas[t]
    else:
       _a					:  Tuple						=  1 - alpha_prod_t / alpha_prod_t_prev
       _a					:  Union[str, Any]						=  1 - beta

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
       _a					:  str						=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
       _a					:  Dict						=  model_output
    else:
       raise ValueError(
           F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
           ' for the UnCLIPScheduler.'      )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       _a					:  str						=  torch.clamp(
           lowerCamelCase_		,      -self.config.clip_sample_range		,      self.config.clip_sample_range      )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _a					:  List[str]						=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
    _a					:  Optional[int]						=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _a					:  List[Any]						=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    _a					:  int						=  0
    if t > 0:
       _a					:  Optional[int]						=  randn_tensor(
           model_output.shape		,      dtype=model_output.dtype		,      generator=lowerCamelCase_		,      device=model_output.device      )

       _a					:  Dict						=  self._get_variance(
           lowerCamelCase_		,      predicted_variance=lowerCamelCase_		,      prev_timestep=lowerCamelCase_		,      )

       if self.variance_type == "fixed_small_log":
          _a					:  Union[str, Any]						=  variance
       elif self.variance_type == "learned_range":
          _a					:  Optional[int]						=  (0.5 * variance).exp()
       else:
          raise ValueError(
              F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
              ' for the UnCLIPScheduler.'      )

       _a					:  Dict						=  variance * variance_noise

    _a					:  Optional[int]						=  pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample,)

    return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_		,      pred_original_sample=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    torch.FloatTensor:
    # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
    _a					:  Union[str, Any]						=  self.alphas_cumprod.to(device=original_samples.device		,      dtype=original_samples.dtype      )
    _a					:  Dict						=  timesteps.to(original_samples.device      )

    _a					:  List[str]						=  alphas_cumprod[timesteps] ** 0.5
    _a					:  Tuple						=  sqrt_alpha_prod.flatten()
    while len(sqrt_alpha_prod.shape      ) < len(original_samples.shape      ):
       _a					:  str						=  sqrt_alpha_prod.unsqueeze(-1      )

    _a					:  int						=  (1 - alphas_cumprod[timesteps]) ** 0.5
    _a					:  Dict						=  sqrt_one_minus_alpha_prod.flatten()
    while len(sqrt_one_minus_alpha_prod.shape      ) < len(original_samples.shape      ):
       _a					:  List[str]						=  sqrt_one_minus_alpha_prod.unsqueeze(-1      )

    _a					:  Optional[Any]						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples






'''simple docstring'''




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  List[Any]						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  List[Any]						=  cos(A      )
   _a					:  Union[str, Any]						=  _sin / (2 * q_factor)

   _a					:  Dict						=  (1 - _cos) / 2
   _a					:  Any						=  1 - _cos

   _a					:  Any						=  1 + alpha
   _a					:  int						=  -2 * _cos
   _a					:  str						=  1 - alpha

   _a					:  Optional[Any]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  int						=  tau * frequency / samplerate
   _a					:  int						=  sin(A      )
   _a					:  Union[str, Any]						=  cos(A      )
   _a					:  int						=  _sin / (2 * q_factor)

   _a					:  Dict						=  (1 + _cos) / 2
   _a					:  int						=  -1 - _cos

   _a					:  Optional[int]						=  1 + alpha
   _a					:  str						=  -2 * _cos
   _a					:  Dict						=  1 - alpha

   _a					:  List[str]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  str						=  tau * frequency / samplerate
   _a					:  Dict						=  sin(A      )
   _a					:  int						=  cos(A      )
   _a					:  Dict						=  _sin / (2 * q_factor)

   _a					:  List[Any]						=  _sin / 2
   _a					:  List[str]						=  0
   _a					:  Dict						=  -ba

   _a					:  List[Any]						=  1 + alpha
   _a					:  Union[str, Any]						=  -2 * _cos
   _a					:  List[Any]						=  1 - alpha

   _a					:  int						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A = 1 / sqrt(2      )      ):
   '''simple docstring'''





   _a					:  Optional[Any]						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  Tuple						=  cos(A      )
   _a					:  Dict						=  _sin / (2 * q_factor)

   _a					:  List[Any]						=  1 - alpha
   _a					:  int						=  -2 * _cos
   _a					:  List[Any]						=  1 + alpha

   _a					:  str						=  IIRFilter(2      )
   filt.set_coefficients([ba, ba, ba]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  tau * frequency / samplerate
   _a					:  str						=  sin(A      )
   _a					:  str						=  cos(A      )
   _a					:  List[Any]						=  _sin / (2 * q_factor)
   _a					:  Optional[Any]						=  1_0 ** (gain_db / 4_0)

   _a					:  Dict						=  1 + alpha * big_a
   _a					:  str						=  -2 * _cos
   _a					:  Tuple						=  1 - alpha * big_a
   _a					:  Tuple						=  1 + alpha / big_a
   _a					:  str						=  -2 * _cos
   _a					:  Union[str, Any]						=  1 - alpha / big_a

   _a					:  Optional[int]						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Optional[int]						=  tau * frequency / samplerate
   _a					:  List[str]						=  sin(A      )
   _a					:  Tuple						=  cos(A      )
   _a					:  Union[str, Any]						=  _sin / (2 * q_factor)
   _a					:  str						=  1_0 ** (gain_db / 4_0)
   _a					:  Optional[Any]						=  (big_a + 1) - (big_a - 1) * _cos
   _a					:  List[str]						=  (big_a + 1) + (big_a - 1) * _cos
   _a					:  List[Any]						=  (big_a - 1) - (big_a + 1) * _cos
   _a					:  Dict						=  (big_a - 1) + (big_a + 1) * _cos
   _a					:  Tuple						=  2 * sqrt(A      ) * alpha

   _a					:  Any						=  big_a * (pmc + aaa)
   _a					:  Optional[int]						=  2 * big_a * mpc
   _a					:  Dict						=  big_a * (pmc - aaa)
   _a					:  List[str]						=  ppmc + aaa
   _a					:  int						=  -2 * pmpc
   _a					:  Tuple						=  ppmc - aaa

   _a					:  Tuple						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt







def    UpperCAmelCase_  (       A		,	A		,	A		,	A = 1 / sqrt(2      )		,	):
   '''simple docstring'''





   _a					:  Dict						=  tau * frequency / samplerate
   _a					:  Tuple						=  sin(A      )
   _a					:  Any						=  cos(A      )
   _a					:  int						=  _sin / (2 * q_factor)
   _a					:  str						=  1_0 ** (gain_db / 4_0)
   _a					:  List[Any]						=  (big_a + 1) - (big_a - 1) * _cos
   _a					:  Union[str, Any]						=  (big_a + 1) + (big_a - 1) * _cos
   _a					:  List[Any]						=  (big_a - 1) - (big_a + 1) * _cos
   _a					:  List[str]						=  (big_a - 1) + (big_a + 1) * _cos
   _a					:  Union[str, Any]						=  2 * sqrt(A      ) * alpha

   _a					:  Optional[Any]						=  big_a * (ppmc + aaa)
   _a					:  List[str]						=  -2 * big_a * pmpc
   _a					:  Any						=  big_a * (ppmc - aaa)
   _a					:  List[Any]						=  pmc + aaa
   _a					:  Tuple						=  2 * mpc
   _a					:  List[Any]						=  pmc - aaa

   _a					:  str						=  IIRFilter(2      )
   filt.set_coefficients([aa, aa, aa]		,	[ba, ba, ba]      )
   return filt


