




'''simple docstring'''



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin






class 				_a							(       __snake_case      ,	__snake_case      ):





       '''simple docstring'''






       A		:							Dict								=		1







       @register_to_config
       def __init__(	self,				A=2_000,				A=0.1,				A=20,				A=1E-3   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		None
             SCREAMING_SNAKE_CASE					:	Any   =		None
             SCREAMING_SNAKE_CASE					:	Dict   =		None







       def     UpperCamelCase_				(	self,				A,				A = None   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		torch.linspace(1,				self.config.sampling_eps,				A_,				device=A_   )







       def     UpperCamelCase_				(	self,				A,				A,				A,				A=None   ):







             '''simple docstring'''







             if self.timesteps is None:
                   raise ValueError(
                       '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             SCREAMING_SNAKE_CASE					:	int   =		(
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             SCREAMING_SNAKE_CASE					:	List[str]   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		std.flatten()
             while len(std.shape   ) < len(score.shape   ):
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		std.unsqueeze(-1   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		-score / std

             # compute
             SCREAMING_SNAKE_CASE					:	str   =		-1.0 / len(self.timesteps   )

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             SCREAMING_SNAKE_CASE					:	Tuple   =		beta_t.flatten()
             while len(beta_t.shape   ) < len(x.shape   ):
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		beta_t.unsqueeze(-1   )
             SCREAMING_SNAKE_CASE					:	List[Any]   =		-0.5 * beta_t * x

             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.sqrt(A_   )
             SCREAMING_SNAKE_CASE					:	Any   =		drift - diffusion**2 * score
             SCREAMING_SNAKE_CASE					:	List[Any]   =		x + drift * dt

             # add noise
             SCREAMING_SNAKE_CASE					:	str   =		randn_tensor(x.shape,				layout=x.layout,				generator=A_,				device=x.device,				dtype=x.dtype   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		x_mean + diffusion * math.sqrt(-dt   ) * noise

             return x, x_mean







       def __len__(	self   ):







             '''simple docstring'''







             return self.config.num_train_timesteps









from typing import List, Optional, Tuple, Union

import torch

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


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



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					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	:					Union[str, 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	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (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	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
