


"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



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


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Optional[int]=0.999       ,		lowerCamelCase__	:							str="cosine"       ,		):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(lowerCamelCase__	:							Optional[int]  ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(lowerCamelCase__	:							List[Any]  ):
   return math.exp(t * -12.0  )

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

 lowercase__       :       Dict				=						[]
 for i in range(lowerCamelCase__  ):
  lowercase__       :       List[str]				=						i / num_diffusion_timesteps
  lowercase__       :       Dict				=						(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(lowerCamelCase__  ) / alpha_bar_fn(lowerCamelCase__  )       ,		lowerCamelCase__  )  )
 return torch.tensor(lowerCamelCase__       ,		dtype=torch.floataa  )






class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Tuple									=    1


   @register_to_config
   def __init__(						self ,     lowerCamelCase__ = 1000 ,     lowerCamelCase__ = 0.0001 ,     lowerCamelCase__ = 0.02 ,     lowerCamelCase__ = "linear" ,     lowerCamelCase__ = None ,     lowerCamelCase__ = True ,     lowerCamelCase__ = True ,     lowerCamelCase__ = 0 ,     lowerCamelCase__ = "epsilon" ,     lowerCamelCase__ = 1.0 ,     **lowerCamelCase__ ,     )      ->							int:
    if kwargs.get("""set_alpha_to_one""" ,     lowerCamelCase__						) is not None:
     lowercase__       :       int				=						(
         """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
     )
     deprecate("""set_alpha_to_one""" ,     """1.0.0""" ,     lowerCamelCase__ ,     standard_warn=lowerCamelCase__						)
     lowercase__       :       Tuple				=						kwargs["""set_alpha_to_one"""]
    if trained_betas is not None:
     lowercase__       :       Any				=						torch.tensor(lowerCamelCase__ ,     dtype=torch.floataa						)
    elif beta_schedule == "linear":
     lowercase__       :       Any				=						torch.linspace(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     dtype=torch.floataa						)
    elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     lowercase__       :       List[Any]				=						(
         torch.linspace(beta_start**0.5 ,     beta_end**0.5 ,     lowerCamelCase__ ,     dtype=torch.floataa						) ** 2
     )
    elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     lowercase__       :       Optional[int]				=						betas_for_alpha_bar(lowerCamelCase__						)
    else:
     raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

    lowercase__       :       int				=						1.0 - self.betas
    lowercase__       :       Optional[int]				=						torch.cumprod(self.alphas ,     dim=0						)

    # At every step in inverted ddim, we are looking into the next alphas_cumprod
    # For the final step, there is no next alphas_cumprod, and the index is out of bounds
    # `set_alpha_to_zero` decides whether we set this parameter simply to zero
    # in this case, self.step() just output the predicted noise
    # or whether we use the final alpha of the "non-previous" one.
    lowercase__       :       List[Any]				=						torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

    # standard deviation of the initial noise distribution
    lowercase__       :       List[Any]				=						1.0

    # setable values
    lowercase__       :       Union[str, Any]				=						None
    lowercase__       :       int				=						torch.from_numpy(np.arange(0 ,     lowerCamelCase__						).copy().astype(np.intaa						)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							torch.FloatTensor:
    return sample


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Optional[Any]:

    if num_inference_steps > self.config.num_train_timesteps:
     raise ValueError(
         F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
         F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
         F''' maximal {self.config.num_train_timesteps} timesteps.'''						)

    lowercase__       :       Optional[int]				=						num_inference_steps
    lowercase__       :       Tuple				=						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
    lowercase__       :       Any				=						(np.arange(0 ,     lowerCamelCase__						) * step_ratio).round().copy().astype(np.intaa						)
    lowercase__       :       str				=						torch.from_numpy(lowerCamelCase__						).to(lowerCamelCase__						)
    self.timesteps += self.config.steps_offset


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ = 0.0 ,     lowerCamelCase__ = False ,     lowerCamelCase__ = None ,     lowerCamelCase__ = True ,     )      ->							Union[DDIMSchedulerOutput, Tuple]:
    # 1. get previous step value (=t+1)
    lowercase__       :       Dict				=						timestep + self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    # change original implementation to exactly match noise levels for analogous forward process
    lowercase__       :       Optional[int]				=						self.alphas_cumprod[timestep]
    lowercase__       :       Tuple				=						(
        self.alphas_cumprod[prev_timestep]
        if prev_timestep < self.config.num_train_timesteps
        else self.final_alpha_cumprod
    )

    lowercase__       :       Union[str, Any]				=						1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    if self.config.prediction_type == "epsilon":
     lowercase__       :       Union[str, Any]				=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     lowercase__       :       Dict				=						model_output
    elif self.config.prediction_type == "sample":
     lowercase__       :       str				=						model_output
     lowercase__       :       Optional[Any]				=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
    elif self.config.prediction_type == "v_prediction":
     lowercase__       :       int				=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     lowercase__       :       List[Any]				=						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
    else:
     raise ValueError(
         F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
         """ `v_prediction`"""						)

    # 4. Clip or threshold "predicted x_0"
    if self.config.clip_sample:
     lowercase__       :       Optional[Any]				=						pred_original_sample.clamp(
         -self.config.clip_sample_range ,     self.config.clip_sample_range						)

    # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    lowercase__       :       str				=						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

    # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    lowercase__       :       Optional[int]				=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if not return_dict:
     return (prev_sample, pred_original_sample)
    return DDIMSchedulerOutput(prev_sample=lowerCamelCase__ ,     pred_original_sample=lowerCamelCase__						)







   def __len__(						self						)      ->							Dict:
    return self.config.num_train_timesteps



"""simple docstring"""


from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Union[str, Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						_sin / (2 * q_factor)

 lowercase__       :       str				=						(1 - _cos) / 2
 lowercase__       :       Optional[Any]				=						1 - _cos

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

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


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Any				=						sin(lowerCamelCase__  )
 lowercase__       :       Any				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       Tuple				=						(1 + _cos) / 2
 lowercase__       :       str				=						-1 - _cos

 lowercase__       :       Dict				=						1 + alpha
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       int				=						1 - alpha

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


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       int				=						cos(lowerCamelCase__  )
 lowercase__       :       Dict				=						_sin / (2 * q_factor)

 lowercase__       :       Dict				=						_sin / 2
 lowercase__       :       Union[str, Any]				=						0
 lowercase__       :       int				=						-ba

 lowercase__       :       List[Any]				=						1 + alpha
 lowercase__       :       Tuple				=						-2 * _cos
 lowercase__       :       List[str]				=						1 - alpha

 lowercase__       :       Dict				=						IIRFilter(2  )
 filt.set_coefficients([aa, aa, aa]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )  ):
 lowercase__       :       str				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       Tuple				=						_sin / (2 * q_factor)

 lowercase__       :       List[Any]				=						1 - alpha
 lowercase__       :       Any				=						-2 * _cos
 lowercase__       :       Optional[Any]				=						1 + alpha

 lowercase__       :       List[Any]				=						IIRFilter(2  )
 filt.set_coefficients([ba, ba, ba]       ,		[ba, ba, ba]  )
 return filt


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Tuple				=						tau * frequency / samplerate
 lowercase__       :       str				=						sin(lowerCamelCase__  )
 lowercase__       :       List[str]				=						cos(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						_sin / (2 * q_factor)
 lowercase__       :       Optional[Any]				=						10 ** (gain_db / 40)

 lowercase__       :       Optional[Any]				=						1 + alpha * big_a
 lowercase__       :       Dict				=						-2 * _cos
 lowercase__       :       Tuple				=						1 - alpha * big_a
 lowercase__       :       List[str]				=						1 + alpha / big_a
 lowercase__       :       List[Any]				=						-2 * _cos
 lowercase__       :       Optional[int]				=						1 - alpha / big_a

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


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       Optional[int]				=						tau * frequency / samplerate
 lowercase__       :       Dict				=						sin(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       int				=						_sin / (2 * q_factor)
 lowercase__       :       Union[str, Any]				=						10 ** (gain_db / 40)
 lowercase__       :       Dict				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Union[str, Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       List[Any]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       Dict				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[int]				=						2 * sqrt(lowerCamelCase__  ) * alpha

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

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






def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float = 1 / sqrt(2  )       ,		):
 lowercase__       :       int				=						tau * frequency / samplerate
 lowercase__       :       List[Any]				=						sin(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						cos(lowerCamelCase__  )
 lowercase__       :       List[str]				=						_sin / (2 * q_factor)
 lowercase__       :       int				=						10 ** (gain_db / 40)
 lowercase__       :       str				=						(big_a + 1) - (big_a - 1) * _cos
 lowercase__       :       Optional[Any]				=						(big_a + 1) + (big_a - 1) * _cos
 lowercase__       :       Optional[int]				=						(big_a - 1) - (big_a + 1) * _cos
 lowercase__       :       int				=						(big_a - 1) + (big_a + 1) * _cos
 lowercase__       :       Optional[Any]				=						2 * sqrt(lowerCamelCase__  ) * alpha

 lowercase__       :       str				=						big_a * (ppmc + aaa)
 lowercase__       :       Union[str, Any]				=						-2 * big_a * pmpc
 lowercase__       :       List[Any]				=						big_a * (ppmc - aaa)
 lowercase__       :       str				=						pmc + aaa
 lowercase__       :       int				=						2 * mpc
 lowercase__       :       Any				=						pmc - aaa

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