


'''simple docstring'''


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__magic_name__			=      {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__magic_name__			=      {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__magic_name__			=      {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__magic_name__			=      {
    'num_train_timesteps': 40,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

__magic_name__			=      {
    'num_train_timesteps': 201,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

__magic_name__			=      {
    'num_train_timesteps': 151,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      if isinstance(lowerCamelCase							,						lowerCamelCase):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError("""boolean value expected""")




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple=False):
      A_						:  Dict          =				checkpoint[F'{old_prefix}.in_layers.0.weight']
      A_						:  Optional[int]          =				checkpoint[F'{old_prefix}.in_layers.0.bias']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.in_layers.2.weight']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.in_layers.2.bias']
      A_						:  List[str]          =				checkpoint[F'{old_prefix}.emb_layers.1.weight']
      A_						:  int          =				checkpoint[F'{old_prefix}.emb_layers.1.bias']
      A_						:  int          =				checkpoint[F'{old_prefix}.out_layers.0.weight']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.out_layers.0.bias']
      A_						:  Optional[Any]          =				checkpoint[F'{old_prefix}.out_layers.3.weight']
      A_						:  Optional[int]          =				checkpoint[F'{old_prefix}.out_layers.3.bias']

      if has_skip:
            A_						:  str          =				checkpoint[F'{old_prefix}.skip_connection.weight']
            A_						:  List[str]          =				checkpoint[F'{old_prefix}.skip_connection.bias']

      return new_checkpoint




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[Any]=None):
      A_   ,					A_   ,					A_						:  Optional[Any]          =				checkpoint[F'{old_prefix}.qkv.weight'].chunk(3							,						dim=0)
      A_   ,					A_   ,					A_						:  List[Any]          =				checkpoint[F'{old_prefix}.qkv.bias'].chunk(3							,						dim=0)

      A_						:  List[Any]          =				checkpoint[F'{old_prefix}.norm.weight']
      A_						:  Tuple          =				checkpoint[F'{old_prefix}.norm.bias']

      A_						:  Union[str, Any]          =				weight_q.squeeze(-1).squeeze(-1)
      A_						:  Dict          =				bias_q.squeeze(-1).squeeze(-1)
      A_						:  int          =				weight_k.squeeze(-1).squeeze(-1)
      A_						:  Tuple          =				bias_k.squeeze(-1).squeeze(-1)
      A_						:  Optional[Any]          =				weight_v.squeeze(-1).squeeze(-1)
      A_						:  Dict          =				bias_v.squeeze(-1).squeeze(-1)

      A_						:  int          =				(
          checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1).squeeze(-1)
      )
      A_						:  int          =				checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1).squeeze(-1)

      return new_checkpoint




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Tuple):
      A_						:  List[Any]          =				torch.load(lowerCamelCase							,						map_location="""cpu""")
      A_						:  Optional[Any]          =				{}

      A_						:  List[Any]          =				checkpoint["""time_embed.0.weight"""]
      A_						:  Dict          =				checkpoint["""time_embed.0.bias"""]
      A_						:  Optional[Any]          =				checkpoint["""time_embed.2.weight"""]
      A_						:  Any          =				checkpoint["""time_embed.2.bias"""]

      if unet_config["num_class_embeds"] is not None:
            A_						:  Union[str, Any]          =				checkpoint["""label_emb.weight"""]

      A_						:  Union[str, Any]          =				checkpoint["""input_blocks.0.0.weight"""]
      A_						:  List[str]          =				checkpoint["""input_blocks.0.0.bias"""]

      A_						:  Optional[int]          =				unet_config["""down_block_types"""]
      A_						:  Union[str, Any]          =				unet_config["""layers_per_block"""]
      A_						:  Optional[Any]          =				unet_config["""attention_head_dim"""]
      A_						:  Dict          =				unet_config["""block_out_channels"""]
      A_						:  Union[str, Any]          =				1
      A_						:  Optional[int]          =				channels_list[0]

      for i, layer_type in enumerate(lowerCamelCase):
            A_						:  Any          =				channels_list[i]
            A_						:  Tuple          =				current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(lowerCamelCase):
                        A_						:  List[str]          =				F'down_blocks.{i}.resnets.{j}'
                        A_						:  Optional[int]          =				F'input_blocks.{current_layer}.0'
                        A_						:  Dict          =				True if j == 0 and downsample_block_has_skip else False
                        A_						:  Dict          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						has_skip=lowerCamelCase)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(lowerCamelCase):
                        A_						:  Optional[Any]          =				F'down_blocks.{i}.resnets.{j}'
                        A_						:  Optional[Any]          =				F'input_blocks.{current_layer}.0'
                        A_						:  Optional[int]          =				True if j == 0 and downsample_block_has_skip else False
                        A_						:  List[str]          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						has_skip=lowerCamelCase)
                        A_						:  str          =				F'down_blocks.{i}.attentions.{j}'
                        A_						:  List[str]          =				F'input_blocks.{current_layer}.1'
                        A_						:  Optional[int]          =				convert_attention(
                            lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
                        current_layer += 1

            if i != len(lowerCamelCase) - 1:
                  A_						:  str          =				F'down_blocks.{i}.downsamplers.0'
                  A_						:  List[str]          =				F'input_blocks.{current_layer}.0'
                  A_						:  Tuple          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
                  current_layer += 1

            A_						:  str          =				current_channels

      # hardcoded the mid-block for now
      A_						:  Optional[Any]          =				"""mid_block.resnets.0"""
      A_						:  Dict          =				"""middle_block.0"""
      A_						:  Any          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
      A_						:  int          =				"""mid_block.attentions.0"""
      A_						:  Dict          =				"""middle_block.1"""
      A_						:  Optional[int]          =				convert_attention(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
      A_						:  List[Any]          =				"""mid_block.resnets.1"""
      A_						:  Dict          =				"""middle_block.2"""
      A_						:  Optional[int]          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      A_						:  Optional[Any]          =				0
      A_						:  int          =				unet_config["""up_block_types"""]

      for i, layer_type in enumerate(lowerCamelCase):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1):
                        A_						:  Tuple          =				F'up_blocks.{i}.resnets.{j}'
                        A_						:  str          =				F'output_blocks.{current_layer}.0'
                        A_						:  Tuple          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						has_skip=lowerCamelCase)
                        current_layer += 1

                  if i != len(lowerCamelCase) - 1:
                        A_						:  int          =				F'up_blocks.{i}.upsamplers.0'
                        A_						:  Tuple          =				F'output_blocks.{current_layer-1}.1'
                        A_						:  Tuple          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1):
                        A_						:  Tuple          =				F'up_blocks.{i}.resnets.{j}'
                        A_						:  Union[str, Any]          =				F'output_blocks.{current_layer}.0'
                        A_						:  Union[str, Any]          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						has_skip=lowerCamelCase)
                        A_						:  List[str]          =				F'up_blocks.{i}.attentions.{j}'
                        A_						:  Optional[Any]          =				F'output_blocks.{current_layer}.1'
                        A_						:  int          =				convert_attention(
                            lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
                        current_layer += 1

                  if i != len(lowerCamelCase) - 1:
                        A_						:  str          =				F'up_blocks.{i}.upsamplers.0'
                        A_						:  List[Any]          =				F'output_blocks.{current_layer-1}.2'
                        A_						:  Dict          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      A_						:  str          =				checkpoint["""out.0.weight"""]
      A_						:  Dict          =				checkpoint["""out.0.bias"""]
      A_						:  Optional[Any]          =				checkpoint["""out.2.weight"""]
      A_						:  int          =				checkpoint["""out.2.bias"""]

      return new_checkpoint


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()

     parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
     parser.add_argument(
         '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
     )
     parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

     __magic_name__			=      parser.parse_args()
     __magic_name__			=      strabool(args.class_cond)

     __magic_name__			=      os.path.basename(args.unet_path)
     print(f"""Checkpoint: {ckpt_name}""")

     # Get U-Net config
     if "imagenet64" in ckpt_name:
          __magic_name__			=      IMAGENET_64_UNET_CONFIG
     elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
          __magic_name__			=      LSUN_256_UNET_CONFIG
     elif "test" in ckpt_name:
          __magic_name__			=      TEST_UNET_CONFIG
     else:
          raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

     if not args.class_cond:
          __magic_name__			=      None

     __magic_name__			=      con_pt_to_diffuser(args.unet_path, unet_config)

     __magic_name__			=      UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          __magic_name__			=      CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          __magic_name__			=      CT_IMAGENET_64_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
          __magic_name__			=      CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

     __magic_name__			=      CMStochasticIterativeScheduler(**scheduler_config)

     __magic_name__			=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)




'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_features""", """is_longer"""]






  def __init__(							self :       Dict	,_a :       Optional[int]=64	,_a :       List[Any]=48000	,_a :       str=480	,_a :       Optional[Any]=10	,_a :       Optional[int]=1024	,_a :       Tuple=0.0	,_a :       str=False	,_a :       float = 0	,_a :       float = 14000	,_a :       int = None	,_a :       str = "fusion"	,_a :       str = "repeatpad"	,**_a :       Tuple	,):

        '''simple docstring'''




        super().__init__(
            feature_size=_a	,sampling_rate=_a	,padding_value=_a	,return_attention_mask=_a	,**_a	,)
        A_						:  Tuple          =				top_db
        A_						:  Tuple          =				truncation
        A_						:  Optional[Any]          =				padding
        A_						:  Optional[int]          =				fft_window_size
        A_						:  Dict          =				(fft_window_size >> 1) + 1
        A_						:  Any          =				hop_length
        A_						:  List[Any]          =				max_length_s
        A_						:  Tuple          =				max_length_s * sampling_rate
        A_						:  Tuple          =				sampling_rate
        A_						:  Optional[int]          =				frequency_min
        A_						:  Tuple          =				frequency_max
        A_						:  Tuple          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm=_a	,mel_scale="""htk"""	,)
        A_						:  Dict          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm="""slaney"""	,mel_scale="""slaney"""	,)






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				copy.deepcopy(self.__dict__ )
        A_						:  Tuple          =				self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
        return output






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[np.array] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				spectrogram(
            _a	,window_function(self.fft_window_size	,"""hann""" )	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=_a	,log_mel="""dB"""	,)
        return log_mel_spectrogram.T






  def        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				np.array_split(list(range(0	,total_frames - chunk_frames + 1 ) )	,3 )
        if len(ranges[1] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  List[Any]          =				[0]
        if len(ranges[2] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  int          =				[0]
        # randomly choose index for each part
        A_						:  List[str]          =				np.random.choice(ranges[0] )
        A_						:  int          =				np.random.choice(ranges[1] )
        A_						:  Optional[int]          =				np.random.choice(ranges[2] )

        A_						:  Tuple          =				mel[idx_front : idx_front + chunk_frames, :]
        A_						:  Dict          =				mel[idx_middle : idx_middle + chunk_frames, :]
        A_						:  Dict          =				mel[idx_back : idx_back + chunk_frames, :]

        A_						:  Optional[int]          =				torch.tensor(mel[None, None, :] )
        A_						:  Dict          =				torch.nn.functional.interpolate(
            _a	,size=[chunk_frames, 64]	,mode="""bilinear"""	,align_corners=_a )
        A_						:  str          =				mel_shrink[0][0].numpy()
        A_						:  Tuple          =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0 )
        return mel_fusion






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[Any]	,_a :       int	,_a :       Dict ):

        '''simple docstring'''




        if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                    A_						:  Dict          =				True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    A_						:  Tuple          =				len(_a ) - max_length
                    A_						:  Optional[int]          =				np.random.randint(0	,overflow + 1 )
                    A_						:  List[Any]          =				waveform[idx : idx + max_length]
                    A_						:  Optional[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]
              elif truncation == "fusion":
                    A_						:  Dict          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Tuple          =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    A_						:  Optional[int]          =				mel.shape[0]
                    if chunk_frames == total_frames:
                          # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                          # In this case, we just use the whole audio.
                          A_						:  Optional[int]          =				np.stack([mel, mel, mel, mel]	,axis=0 )
                          A_						:  str          =				False
                    else:
                          A_						:  str          =				self._random_mel_fusion(_a	,_a	,_a )
                          A_						:  Optional[Any]          =				True
              else:
                    raise NotImplementedError(f'data_truncating {truncation} not implemented' )

        else:
              A_						:  Optional[int]          =				False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                    if padding == "repeat":
                          A_						:  int          =				int(max_length / len(_a ) )
                          A_						:  Any          =				np.stack(np.tile(_a	,n_repeat + 1 ) )[:max_length]
                    if padding == "repeatpad":
                          A_						:  List[str]          =				int(max_length / len(_a ) )
                          A_						:  Optional[Any]          =				np.stack(np.tile(_a	,_a ) )
                    A_						:  Any          =				np.pad(_a	,(0, max_length - waveform.shape[0])	,mode="""constant"""	,constant_values=0 )

              if truncation == "fusion":
                    A_						:  List[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Optional[Any]          =				np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0 )
              else:
                    A_						:  Union[str, Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]

        return input_mel, longer






  def __call__(							self :       List[Any]	,_a :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_a :       str = None	,_a :       Optional[str] = None	,_a :       Optional[int] = None	,_a :       Optional[int] = None	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Any	,):

        '''simple docstring'''




        A_						:  List[str]          =				truncation if truncation is not None else self.truncation
        A_						:  List[Any]          =				padding if padding else self.padding

        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                        f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                        f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
        else:
              logger.warning(
                  """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                  """Failing to do so can result in silent errors that might be hard to debug.""" )

        A_						:  Any          =				isinstance(_a	,np.ndarray ) and len(raw_speech.shape ) > 1
        if is_batched_numpy and len(raw_speech.shape ) > 2:
              raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
        A_						:  int          =				is_batched_numpy or (
            isinstance(_a	,(list, tuple) ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list) ))
        )

        if is_batched:
              A_						:  Optional[int]          =				[np.asarray(_a	,dtype=np.floataa ) for speech in raw_speech]
        elif not is_batched and not isinstance(_a	,np.ndarray ):
              A_						:  str          =				np.asarray(_a	,dtype=np.floataa )
        elif isinstance(_a	,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
              A_						:  Tuple          =				raw_speech.astype(np.floataa )

        # always return batch
        if not is_batched:
              A_						:  Any          =				[np.asarray(_a )]

        # convert to mel spectrogram, truncate and pad if needed.
        A_						:  str          =				[
            self._get_input_mel(_a	,max_length if max_length else self.nb_max_samples	,_a	,_a )
            for waveform in raw_speech
        ]

        A_						:  int          =				[]
        A_						:  Any          =				[]
        for mel, longer in padded_inputs:
              input_mel.append(_a )
              is_longer.append(_a )

        if truncation == "fusion" and sum(_a ) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              A_						:  List[Any]          =				np.random.randint(0	,len(_a ) )
              A_						:  List[str]          =				True

        if isinstance(input_mel[0]	,_a ):
              A_						:  Tuple          =				[np.asarray(_a	,dtype=np.floataa ) for feature in input_mel]

        # is_longer is a list of bool
        A_						:  List[str]          =				[[longer] for longer in is_longer]

        A_						:  Optional[Any]          =				{"""input_features""": input_mel, """is_longer""": is_longer}
        A_						:  int          =				BatchFeature(_a )

        if return_tensors is not None:
              A_						:  int          =				input_features.convert_to_tensors(_a )

        return input_features
