






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case      :      Any             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Tuple:

      """simple docstring"""



      lowerCAmelCase__        =  DPTConfig()

      if "large" in checkpoint_url:
            lowerCAmelCase__        =  1024
            lowerCAmelCase__        =  4096
            lowerCAmelCase__        =  24
            lowerCAmelCase__        =  16
            lowerCAmelCase__        =  [5, 11, 17, 23]
            lowerCAmelCase__        =  [256, 512, 1024, 1024]
            lowerCAmelCase__        =  (1, 384, 384)

      if "ade" in checkpoint_url:
            lowerCAmelCase__        =  True

            lowerCAmelCase__        =  150
            lowerCAmelCase__        =  'huggingface/label-files'
            lowerCAmelCase__        =  'ade20k-id2label.json'
            lowerCAmelCase__        =  json.load(open(cached_download(hf_hub_url(UpperCamelCase_      ,    UpperCamelCase_      ,    repo_type='dataset'						)						)      ,    'r'						)						)
            lowerCAmelCase__        =  {int(UpperCamelCase_						): v for k, v in idalabel.items()}
            lowerCAmelCase__        =  idalabel
            lowerCAmelCase__        =  {v: k for k, v in idalabel.items()}
            lowerCAmelCase__        =  [1, 150, 480, 480]

      return config, expected_shape







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				Optional[int]:

      """simple docstring"""



      lowerCAmelCase__        =  ['pretrained.model.head.weight', 'pretrained.model.head.bias']
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Optional[Any]:

      """simple docstring"""



      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            lowerCAmelCase__        =  name.replace('pretrained.model'      ,    'dpt.encoder'						)
      if "pretrained.model" in name:
            lowerCAmelCase__        =  name.replace('pretrained.model'      ,    'dpt.embeddings'						)
      if "patch_embed" in name:
            lowerCAmelCase__        =  name.replace('patch_embed'      ,    'patch_embeddings'						)
      if "pos_embed" in name:
            lowerCAmelCase__        =  name.replace('pos_embed'      ,    'position_embeddings'						)
      if "attn.proj" in name:
            lowerCAmelCase__        =  name.replace('attn.proj'      ,    'attention.output.dense'						)
      if "proj" in name and "project" not in name:
            lowerCAmelCase__        =  name.replace('proj'      ,    'projection'						)
      if "blocks" in name:
            lowerCAmelCase__        =  name.replace('blocks'      ,    'layer'						)
      if "mlp.fc1" in name:
            lowerCAmelCase__        =  name.replace('mlp.fc1'      ,    'intermediate.dense'						)
      if "mlp.fc2" in name:
            lowerCAmelCase__        =  name.replace('mlp.fc2'      ,    'output.dense'						)
      if "norm1" in name:
            lowerCAmelCase__        =  name.replace('norm1'      ,    'layernorm_before'						)
      if "norm2" in name:
            lowerCAmelCase__        =  name.replace('norm2'      ,    'layernorm_after'						)
      if "scratch.output_conv" in name:
            lowerCAmelCase__        =  name.replace('scratch.output_conv'      ,    'head'						)
      if "scratch" in name:
            lowerCAmelCase__        =  name.replace('scratch'      ,    'neck'						)
      if "layer1_rn" in name:
            lowerCAmelCase__        =  name.replace('layer1_rn'      ,    'convs.0'						)
      if "layer2_rn" in name:
            lowerCAmelCase__        =  name.replace('layer2_rn'      ,    'convs.1'						)
      if "layer3_rn" in name:
            lowerCAmelCase__        =  name.replace('layer3_rn'      ,    'convs.2'						)
      if "layer4_rn" in name:
            lowerCAmelCase__        =  name.replace('layer4_rn'      ,    'convs.3'						)
      if "refinenet" in name:
            lowerCAmelCase__        =  int(name[len('neck.refinenet'						) : len('neck.refinenet'						) + 1]						)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            lowerCAmelCase__        =  name.replace(F"refinenet{layer_idx}"      ,    F"fusion_stage.layers.{abs(layer_idx-4						)}"						)
      if "out_conv" in name:
            lowerCAmelCase__        =  name.replace('out_conv'      ,    'projection'						)
      if "resConfUnit1" in name:
            lowerCAmelCase__        =  name.replace('resConfUnit1'      ,    'residual_layer1'						)
      if "resConfUnit2" in name:
            lowerCAmelCase__        =  name.replace('resConfUnit2'      ,    'residual_layer2'						)
      if "conv1" in name:
            lowerCAmelCase__        =  name.replace('conv1'      ,    'convolution1'						)
      if "conv2" in name:
            lowerCAmelCase__        =  name.replace('conv2'      ,    'convolution2'						)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess1.0.project.0'      ,    'neck.reassemble_stage.readout_projects.0.0'						)
      if "pretrained.act_postprocess2.0.project.0" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess2.0.project.0'      ,    'neck.reassemble_stage.readout_projects.1.0'						)
      if "pretrained.act_postprocess3.0.project.0" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess3.0.project.0'      ,    'neck.reassemble_stage.readout_projects.2.0'						)
      if "pretrained.act_postprocess4.0.project.0" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess4.0.project.0'      ,    'neck.reassemble_stage.readout_projects.3.0'						)
      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess1.3'      ,    'neck.reassemble_stage.layers.0.projection'						)
      if "pretrained.act_postprocess1.4" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess1.4'      ,    'neck.reassemble_stage.layers.0.resize'						)
      if "pretrained.act_postprocess2.3" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess2.3'      ,    'neck.reassemble_stage.layers.1.projection'						)
      if "pretrained.act_postprocess2.4" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess2.4'      ,    'neck.reassemble_stage.layers.1.resize'						)
      if "pretrained.act_postprocess3.3" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess3.3'      ,    'neck.reassemble_stage.layers.2.projection'						)
      if "pretrained.act_postprocess4.3" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess4.3'      ,    'neck.reassemble_stage.layers.3.projection'						)
      if "pretrained.act_postprocess4.4" in name:
            lowerCAmelCase__        =  name.replace('pretrained.act_postprocess4.4'      ,    'neck.reassemble_stage.layers.3.resize'						)
      if "pretrained" in name:
            lowerCAmelCase__        =  name.replace('pretrained'      ,    'dpt'						)
      if "bn" in name:
            lowerCAmelCase__        =  name.replace('bn'      ,    'batch_norm'						)
      if "head" in name:
            lowerCAmelCase__        =  name.replace('head'      ,    'head.head'						)
      if "encoder.norm" in name:
            lowerCAmelCase__        =  name.replace('encoder.norm'      ,    'layernorm'						)
      if "auxlayer" in name:
            lowerCAmelCase__        =  name.replace('auxlayer'      ,    'auxiliary_head.head'						)

      return name







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Tuple						)					->				int:

      """simple docstring"""



      for i in range(config.num_hidden_layers						):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCAmelCase__        =  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"						)
            lowerCAmelCase__        =  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"						)
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase__        =  in_proj_weight[: config.hidden_size, :]
            lowerCAmelCase__        =  in_proj_bias[: config.hidden_size]
            lowerCAmelCase__        =  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCAmelCase__        =  in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCAmelCase__        =  in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCAmelCase__        =  in_proj_bias[-config.hidden_size :]







def 		_UpperCamelCase				(      )					->				List[str]:

      """simple docstring"""



      lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
      return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]						)					->				Union[str, Any]:

      """simple docstring"""



      lowerCAmelCase__  ,    lowerCAmelCase__        =  get_dpt_config(UpperCamelCase_						)
      # load original state_dict from URL
      lowerCAmelCase__        =  torch.hub.load_state_dict_from_url(UpperCamelCase_      ,    map_location='cpu'						)
      # remove certain keys
      remove_ignore_keys_(UpperCamelCase_						)
      # rename keys
      for key in state_dict.copy().keys():
            lowerCAmelCase__        =  state_dict.pop(UpperCamelCase_						)
            lowerCAmelCase__        =  val
      # read in qkv matrices
      read_in_q_k_v(UpperCamelCase_      ,    UpperCamelCase_						)

      # load HuggingFace model
      lowerCAmelCase__        =  DPTForSemanticSegmentation(UpperCamelCase_						) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase_						)
      model.load_state_dict(UpperCamelCase_						)
      model.eval()

      # Check outputs on an image
      lowerCAmelCase__        =  480 if 'ade' in checkpoint_url else 384
      lowerCAmelCase__        =  DPTImageProcessor(size=UpperCamelCase_						)

      lowerCAmelCase__        =  prepare_img()
      lowerCAmelCase__        =  image_processor(UpperCamelCase_      ,    return_tensors='pt'						)

      # forward pass
      lowerCAmelCase__        =  model(**UpperCamelCase_						).logits if 'ade' in checkpoint_url else model(**UpperCamelCase_						).predicted_depth

      # Assert logits
      lowerCAmelCase__        =  torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]						)
      if "ade" in checkpoint_url:
            lowerCAmelCase__        =  torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]						)
      assert outputs.shape == torch.Size(UpperCamelCase_						)
      assert (
          torch.allclose(outputs[0, 0, :3, :3]      ,    UpperCamelCase_      ,    atol=1e-4						)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]      ,    UpperCamelCase_						)
      )

      Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
      print(F"Saving model to {pytorch_dump_folder_path}"						)
      model.save_pretrained(UpperCamelCase_						)
      print(F"Saving image processor to {pytorch_dump_folder_path}"						)
      image_processor.save_pretrained(UpperCamelCase_						)

      if push_to_hub:
            print('Pushing model to hub...'						)
            model.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase_      ,    UpperCamelCase_						)      ,    organization='nielsr'      ,    commit_message='Add model'      ,    use_temp_dir=UpperCamelCase_      ,    )
            image_processor.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase_      ,    UpperCamelCase_						)      ,    organization='nielsr'      ,    commit_message='Add image processor'      ,    use_temp_dir=UpperCamelCase_      ,    )


if __name__ == "__main__":
     __snake_case      :      Dict             =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
         type=str,
         help="""URL of the original DPT checkpoint you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
     )
     parser.add_argument(
         """--model_name""",
         default="""dpt-large""",
         type=str,
         help="""Name of the model, in case you're pushing to the hub.""",
     )

     __snake_case      :      str             =       parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)













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


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_features''', '''is_longer''']






						def __init__(		self       ,  _UpperCamelCase=64       ,  _UpperCamelCase=4_80_00       ,  _UpperCamelCase=4_80       ,  _UpperCamelCase=10       ,  _UpperCamelCase=10_24       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=False       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = 1_40_00       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "fusion"       ,  _UpperCamelCase = "repeatpad"       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(
												    feature_size=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  padding_value=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  **_UpperCamelCase       ,  )
												lowerCAmelCase__        =  top_db
												lowerCAmelCase__        =  truncation
												lowerCAmelCase__        =  padding
												lowerCAmelCase__        =  fft_window_size
												lowerCAmelCase__        =  (fft_window_size >> 1) + 1
												lowerCAmelCase__        =  hop_length
												lowerCAmelCase__        =  max_length_s
												lowerCAmelCase__        =  max_length_s * sampling_rate
												lowerCAmelCase__        =  sampling_rate
												lowerCAmelCase__        =  frequency_min
												lowerCAmelCase__        =  frequency_max
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm=_UpperCamelCase       ,  mel_scale='htk'       ,  )
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm='slaney'       ,  mel_scale='slaney'       ,  )






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  copy.deepcopy(self.__dict__				)
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  spectrogram(
												    _UpperCamelCase       ,  window_function(self.fft_window_size       ,  'hann'				)       ,  frame_length=self.fft_window_size       ,  hop_length=self.hop_length       ,  power=2.0       ,  mel_filters=_UpperCamelCase       ,  log_mel='dB'       ,  )
												return log_mel_spectrogram.T






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  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
																		lowerCAmelCase__        =  [0]
												if len(ranges[2]				) == 0:
																		# if the audio is too short, we just use the first chunk
																		lowerCAmelCase__        =  [0]
												# randomly choose index for each part
												lowerCAmelCase__        =  np.random.choice(ranges[0]				)
												lowerCAmelCase__        =  np.random.choice(ranges[1]				)
												lowerCAmelCase__        =  np.random.choice(ranges[2]				)

												lowerCAmelCase__        =  mel[idx_front : idx_front + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_middle : idx_middle + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_back : idx_back + chunk_frames, :]

												lowerCAmelCase__        =  torch.tensor(mel[None, None, :]				)
												lowerCAmelCase__        =  torch.nn.functional.interpolate(
												    _UpperCamelCase       ,  size=[chunk_frames, 64]       ,  mode='bilinear'       ,  align_corners=_UpperCamelCase				)
												lowerCAmelCase__        =  mel_shrink[0][0].numpy()
												lowerCAmelCase__        =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,  axis=0				)
												return mel_fusion






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																								lowerCAmelCase__        =  True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								lowerCAmelCase__        =  len(_UpperCamelCase				) - max_length
																								lowerCAmelCase__        =  np.random.randint(0       ,  overflow + 1				)
																								lowerCAmelCase__        =  waveform[idx : idx + max_length]
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters_slaney				)[None, :]
																		elif truncation == "fusion":
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters				)
																								lowerCAmelCase__        =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								lowerCAmelCase__        =  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.
																														lowerCAmelCase__        =  np.stack([mel, mel, mel, mel]       ,  axis=0				)
																														lowerCAmelCase__        =  False
																								else:
																														lowerCAmelCase__        =  self._random_mel_fusion(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
																														lowerCAmelCase__        =  True
																		else:
																								raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

												else:
																		lowerCAmelCase__        =  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":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  n_repeat + 1				)				)[:max_length]
																								if padding == "repeatpad":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  _UpperCamelCase				)				)
																								lowerCAmelCase__        =  np.pad(_UpperCamelCase       ,  (0, max_length - waveform.shape[0])       ,  mode='constant'       ,  constant_values=0				)

																		if truncation == "fusion":
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters				)
																								lowerCAmelCase__        =  np.stack([input_mel, input_mel, input_mel, input_mel]       ,  axis=0				)
																		else:
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters_slaney				)[None, :]

												return input_mel, longer







						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  truncation if truncation is not None else self.truncation
												lowerCAmelCase__        =  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.'				)

												lowerCAmelCase__        =  isinstance(_UpperCamelCase       ,  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}"				)
												lowerCAmelCase__        =  is_batched_numpy or (
												    isinstance(_UpperCamelCase       ,  (list, tuple)				) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list)				))
												)

												if is_batched:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCamelCase       ,  np.ndarray				):
																		lowerCAmelCase__        =  np.asarray(_UpperCamelCase       ,  dtype=np.floataa				)
												elif isinstance(_UpperCamelCase       ,  np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
																		lowerCAmelCase__        =  raw_speech.astype(np.floataa				)

												# always return batch
												if not is_batched:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase				)]

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

												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  []
												for mel, longer in padded_inputs:
																		input_mel.append(_UpperCamelCase				)
																		is_longer.append(_UpperCamelCase				)

												if truncation == "fusion" and sum(_UpperCamelCase				) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		lowerCAmelCase__        =  np.random.randint(0       ,  len(_UpperCamelCase				)				)
																		lowerCAmelCase__        =  True

												if isinstance(input_mel[0]       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for feature in input_mel]

												# is_longer is a list of bool
												lowerCAmelCase__        =  [[longer] for longer in is_longer]

												lowerCAmelCase__        =  {'input_features': input_mel, 'is_longer': is_longer}
												lowerCAmelCase__        =  BatchFeature(_UpperCamelCase				)

												if return_tensors is not None:
																		lowerCAmelCase__        =  input_features.convert_to_tensors(_UpperCamelCase				)

												return input_features





