





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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







class __a (   _snake_case			):





   def __init__(					self				:			Dict				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			List[Any]    ):

      '''simple docstring'''

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





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

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       audio_length_in_s * self.unet.config.sample_rate

      __SCREAMING_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}."""    )

      __SCREAMING_SNAKE_CASE      							=       int(lowerCamelCase    )
      if sample_size % down_scale_factor != 0:
         __SCREAMING_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."""    )
      __SCREAMING_SNAKE_CASE      							=       int(lowerCamelCase    )

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

      __SCREAMING_SNAKE_CASE      							=       randn_tensor(lowerCamelCase				,generator=lowerCamelCase				,device=self.device				,dtype=lowerCamelCase    )

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

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

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

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

      __SCREAMING_SNAKE_CASE      							=       audio[:, :, :original_sample_size]

      if not return_dict:
         return (audio,)

      return AudioPipelineOutput(audios=lowerCamelCase    )












'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SEWForCTC",
        "SEWForSequenceClassification",
        "SEWModel",
        "SEWPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





