


from typing import List, Optional, Tuple, Union

import torch

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


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


class 			__lowerCAmelCase   (       snake_case_    ):






  """simple docstring"""





  def __init__(   self				:				Optional[Any]			, _lowerCAmelCase				:				List[str]			, _lowerCAmelCase				:				Tuple     )      ->       int:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=UpperCAmelCase__			, scheduler=UpperCAmelCase__     )



  @torch.no_grad()
  def __call__(   self				:				List[str]			, _lowerCAmelCase				:				int = 1			, _lowerCAmelCase				:				int = 1_0_0			, _lowerCAmelCase				:				Optional[Union[torch.Generator, List[torch.Generator]]] = None			, _lowerCAmelCase				:				Optional[float] = None			, _lowerCAmelCase				:				bool = True			, )      ->       Union[AudioPipelineOutput, Tuple]:




     """simple docstring"""

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

     snake_case_    			=    audio_length_in_s * self.unet.config.sample_rate

     snake_case_    			=    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}.'''     )

     snake_case_    			=    int(UpperCAmelCase__     )
     if sample_size % down_scale_factor != 0:
        snake_case_    			=    (
            (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."     )
     snake_case_    			=    int(UpperCAmelCase__     )

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

     snake_case_    			=    randn_tensor(UpperCAmelCase__			, generator=UpperCAmelCase__			, device=self.device			, dtype=UpperCAmelCase__     )

     # set step values
     self.scheduler.set_timesteps(UpperCAmelCase__			, device=audio.device     )
     snake_case_    			=    self.scheduler.timesteps.to(UpperCAmelCase__     )

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

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

     snake_case_    			=    audio.clamp(-1			, 1     ).float().cpu().numpy()

     snake_case_    			=    audio[:, :, :original_sample_size]

     if not return_dict:
        return (audio,)

     return AudioPipelineOutput(audios=UpperCAmelCase__     )












"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	[redshift, radiation_density, matter_density, dark_energy]
  if any(p < 0 for p in parameters				):
    raise ValueError("""All input parameters must be positive"""				)

  if any(p > 1 for p in parameters[1:4]				):
    raise ValueError("""Relative densities cannot be greater than one"""				)
  else:
    _a  :       Tuple             =	1 - (matter_density + radiation_density + dark_energy)

    _a  :       int             =	(
        radiation_density * (redshift + 1) ** 4
        + matter_density * (redshift + 1) ** 3
        + curvature * (redshift + 1) ** 2
        + dark_energy
    )

    _a  :       List[str]             =	hubble_constant * e_a ** (1 / 2)
    return hubble


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo LCDM approximation
 _snake_case          =   0.3

 print(
     hubble_parameter(
         hubble_constant=68.3,
         radiation_density=1e-4,
         matter_density=matter_density,
         dark_energy=1 - matter_density,
         redshift=0,
     )
 )

