
'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : List[str]			,							a__  : int    ):
  '''simple docstring'''

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


 @torch.no_grad()
 def __call__(    self  : List[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:
   lowerCAmelCase__     :   Tuple     		=	self.unet.config.sample_size / self.unet.config.sample_rate

  lowerCAmelCase__     :   Tuple     		=	audio_length_in_s * self.unet.config.sample_rate

  lowerCAmelCase__     :   Optional[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}.'''    )

  lowerCAmelCase__     :   Union[str, Any]     		=	int(a__    )
  if sample_size % down_scale_factor != 0:
   lowerCAmelCase__     :   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."    )
  lowerCAmelCase__     :   int     		=	int(a__    )

  lowerCAmelCase__     :   Tuple     		=	next(iter(self.unet.parameters()    )    ).dtype
  lowerCAmelCase__     :   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.'''    )

  lowerCAmelCase__     :   Optional[int]     		=	randn_tensor(a__			,							generator=a__			,							device=self.device			,							dtype=a__    )

  # set step values
  self.scheduler.set_timesteps(a__			,							device=audio.device    )
  lowerCAmelCase__     :   int     		=	self.scheduler.timesteps.to(a__    )

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

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

  lowerCAmelCase__     :   List[str]     		=	audio.clamp(-1			,							1    ).float().cpu().numpy()

  lowerCAmelCase__     :   Tuple     		=	audio[:, :, :original_sample_size]

  if not return_dict:
   return (audio,)

  return AudioPipelineOutput(audios=a__    )







'''simple docstring'''

from ....configuration_utils import PretrainedConfig
from ....utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Tuple	=			"""van"""


 def __init__(    self  : List[Any]			,							a__  : Dict=224			,							a__  : Dict=3			,							a__  : Union[str, Any]=[7, 3, 3, 3]			,							a__  : Optional[Any]=[4, 2, 2, 2]			,							a__  : Optional[Any]=[64, 128, 320, 512]			,							a__  : List[str]=[3, 3, 12, 3]			,							a__  : Any=[8, 8, 4, 4]			,							a__  : Optional[int]="gelu"			,							a__  : List[Any]=0.02			,							a__  : Tuple=1e-6			,							a__  : List[str]=1e-2			,							a__  : List[str]=0.0			,							a__  : List[Any]=0.0			,							**a__  : Tuple			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   Any     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	patch_sizes
  lowerCAmelCase__     :   Dict     		=	strides
  lowerCAmelCase__     :   List[str]     		=	hidden_sizes
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Tuple     		=	mlp_ratios
  lowerCAmelCase__     :   Optional[Any]     		=	hidden_act
  lowerCAmelCase__     :   Dict     		=	initializer_range
  lowerCAmelCase__     :   int     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[Any]     		=	layer_scale_init_value
  lowerCAmelCase__     :   List[str]     		=	drop_path_rate
  lowerCAmelCase__     :   Any     		=	dropout_rate





