


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      lowercase__	(       __lowercase		):






  def __init__(       self   :       Optional[Any]		,    _lowercase   :       Optional[Any]		,    _lowercase   :       int			):




         """simple docstring"""







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






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




         """simple docstring"""







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

         UpperCAmelCase__					    =  audio_length_in_s * self.unet.config.sample_rate

         UpperCAmelCase__					    =  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__					    =  int(_lowercase			)
         if sample_size % down_scale_factor != 0:
                UpperCAmelCase__					    =  (
                    (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__					    =  int(_lowercase			)

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

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

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

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

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

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

         UpperCAmelCase__					    =  audio[:, :, :original_sample_size]

         if not return_dict:
                return (audio,)

         return AudioPipelineOutput(audios=_lowercase			)












'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

					warnings.warn(
					    """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use OwlViTImageProcessor instead."""  ,      __magic_name__  ,      )
					super().__init__(*__magic_name__  ,      **__magic_name__			)


