



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase: Union[str, Any]							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Dict						,      __snake_case			:  str						,      __snake_case			:  Optional[int]     ):
      super().__init__()
      self.register_modules(unet=__snake_case						,      scheduler=__snake_case     )







    @torch.no_grad()
    def __call__(				self			:  Dict						,      __snake_case			:  int = 1						,      __snake_case			:  int = 1_00						,      __snake_case			:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,      __snake_case			:  Optional[float] = None						,      __snake_case			:  bool = True						,      ):

      if audio_length_in_s is None:
        a						: Optional[Any]					       =					self.unet.config.sample_size / self.unet.config.sample_rate

      a						: Optional[int]					       =					audio_length_in_s * self.unet.config.sample_rate

      a						: Optional[int]					       =					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}."""     )

      a						: Any					       =					int(__snake_case     )
      if sample_size % down_scale_factor != 0:
        a						: int					       =					(
            (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.'     )
      a						: Tuple					       =					int(__snake_case     )

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

      a						: Optional[Any]					       =					randn_tensor(__snake_case						,      generator=__snake_case						,      device=self.device						,      dtype=__snake_case     )

      # set step values
      self.scheduler.set_timesteps(__snake_case						,      device=audio.device     )
      a						: Optional[Any]					       =					self.scheduler.timesteps.to(__snake_case     )

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

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

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

      a						: int					       =					audio[:, :, :original_sample_size]

      if not return_dict:
        return (audio,)

      return AudioPipelineOutput(audios=__snake_case     )




'''simple docstring'''






import copy

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


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output