


'''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 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     42
      lowerCAmelCase__		     =     None





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__=0.9_9_9  ,					lowerCAmelCase__="cosine"  ,					)    ->	Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowerCAmelCase__						):
            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(lowerCAmelCase__						):
            return math.exp(t * -1_2.0						)

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

    UpperCAmelCase__			:		int													=				[]
    for i in range(lowerCAmelCase__						):
        UpperCAmelCase__			:		int													=				i / num_diffusion_timesteps
        UpperCAmelCase__			:		int													=				(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 			lowerCamelCase_   (	__a     ,       __a      ):
      lowerCAmelCase__		     =     1






      @register_to_config
      def __init__(					self				:    List[Any]						,						_A				:    int = 1_000						,						_A				:    float = 0.0_0_0_1						,						_A				:    float = 0.0_2						,						_A				:    str = "linear"						,						_A				:    Optional[Union[np.ndarray, List[float]]] = None						,						_A				:    bool = True						,						_A				:    bool = True						,						_A				:    int = 0						,						_A				:    str = "epsilon"						,						_A				:    float = 1.0						,						**_A				:    Optional[int]						,						):


          '''simple docstring'''


          if kwargs.get('''set_alpha_to_one'''						,						_A ) is not None:
              UpperCAmelCase__			:		str													=				(
                  '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
              )
              deprecate('''set_alpha_to_one'''						,						'''1.0.0'''						,						_A						,						standard_warn=_A )
              UpperCAmelCase__			:		Union[str, Any]													=				kwargs['''set_alpha_to_one''']
          if trained_betas is not None:
              UpperCAmelCase__			:		List[Any]													=				torch.tensor(_A						,						dtype=torch.floataa )
          elif beta_schedule == "linear":
              UpperCAmelCase__			:		Tuple													=				torch.linspace(_A						,						_A						,						_A						,						dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase__			:		List[str]													=				(
                  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__			:		Tuple													=				betas_for_alpha_bar(_A )
          else:
              raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

          UpperCAmelCase__			:		Optional[Any]													=				1.0 - self.betas
          UpperCAmelCase__			:		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.
          UpperCAmelCase__			:		Union[str, Any]													=				torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          UpperCAmelCase__			:		str													=				1.0

          # setable values
          UpperCAmelCase__			:		Optional[Any]													=				None
          UpperCAmelCase__			:		Tuple													=				torch.from_numpy(np.arange(0						,						_A ).copy().astype(np.intaa ) )






      def lowercase_							(					self				:    List[str]						,						_A				:    torch.FloatTensor						,						_A				:    Optional[int] = None ):


          '''simple docstring'''


          return sample






      def lowercase_							(					self				:    int						,						_A				:    int						,						_A				:    Union[str, torch.device] = None ):


          '''simple docstring'''


          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.""" )

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






      def lowercase_							(					self				:    Dict						,						_A				:    torch.FloatTensor						,						_A				:    int						,						_A				:    torch.FloatTensor						,						_A				:    float = 0.0						,						_A				:    bool = False						,						_A				:    Optional[torch.FloatTensor] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				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
          UpperCAmelCase__			:		Optional[Any]													=				self.alphas_cumprod[timestep]
          UpperCAmelCase__			:		Tuple													=				(
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          UpperCAmelCase__			:		Dict													=				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":
              UpperCAmelCase__			:		str													=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              UpperCAmelCase__			:		Dict													=				model_output
          elif self.config.prediction_type == "sample":
              UpperCAmelCase__			:		Optional[int]													=				model_output
              UpperCAmelCase__			:		List[str]													=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase__			:		int													=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              UpperCAmelCase__			:		List[str]													=				(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:
              UpperCAmelCase__			:		List[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
          UpperCAmelCase__			:		Tuple													=				(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
          UpperCAmelCase__			:		Optional[Any]													=				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=_A						,						pred_original_sample=_A )






      def __len__(					self				:    Optional[int] ):


          '''simple docstring'''


          return self.config.num_train_timesteps




'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
