

'''simple docstring'''





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()
UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)






def 					__lowercase      (     _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   DPTConfig(embedding_type="""hybrid"""			)

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE :      List[str]       =   1024
            SCREAMING_SNAKE_CASE :      List[str]       =   4096
            SCREAMING_SNAKE_CASE :      Optional[int]       =   24
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   16
            SCREAMING_SNAKE_CASE :      List[str]       =   [5, 11, 17, 23]
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [256, 512, 1024, 1024]
            SCREAMING_SNAKE_CASE :      List[str]       =   (1, 384, 384)

      if "nyu" or "midas" in checkpoint_url:
            SCREAMING_SNAKE_CASE :      Dict       =   768
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE :      Dict       =   [256, 512, 768, 768]
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   150
            SCREAMING_SNAKE_CASE :      int       =   16
            SCREAMING_SNAKE_CASE :      int       =   (1, 384, 384)
            SCREAMING_SNAKE_CASE :      List[str]       =   False
            SCREAMING_SNAKE_CASE :      List[Any]       =   """project"""

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE :      Tuple       =   True
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   768
            SCREAMING_SNAKE_CASE :      int       =   [1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE :      Optional[int]       =   150
            SCREAMING_SNAKE_CASE :      int       =   16
            SCREAMING_SNAKE_CASE :      int       =   """huggingface/label-files"""
            SCREAMING_SNAKE_CASE :      Optional[int]       =   """ade20k-id2label.json"""
            SCREAMING_SNAKE_CASE :      Any       =   json.load(open(cached_download(hf_hub_url(_A						,    _A						,    repo_type="""dataset"""			)			)						,    """r"""			)			)
            SCREAMING_SNAKE_CASE :      Any       =   {int(_A			): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE :      Any       =   idalabel
            SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE :      str       =   [1, 150, 480, 480]

      return config, expected_shape






def 					__lowercase      (     _A			)     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_A						,    _A			)






def 					__lowercase      (     _A			)     ->						Dict:
      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            SCREAMING_SNAKE_CASE :      Tuple       =   name.replace("""pretrained.model"""						,    """dpt.encoder"""			)
      if "pretrained.model" in name:
            SCREAMING_SNAKE_CASE :      Tuple       =   name.replace("""pretrained.model"""						,    """dpt.embeddings"""			)
      if "patch_embed" in name:
            SCREAMING_SNAKE_CASE :      Dict       =   name.replace("""patch_embed"""						,    """"""			)
      if "pos_embed" in name:
            SCREAMING_SNAKE_CASE :      Dict       =   name.replace("""pos_embed"""						,    """position_embeddings"""			)
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE :      int       =   name.replace("""attn.proj"""						,    """attention.output.dense"""			)
      if "proj" in name and "project" not in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""proj"""						,    """projection"""			)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE :      Dict       =   name.replace("""blocks"""						,    """layer"""			)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""mlp.fc1"""						,    """intermediate.dense"""			)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""mlp.fc2"""						,    """output.dense"""			)
      if "norm1" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE :      List[Any]       =   name.replace("""norm1"""						,    """layernorm_before"""			)
      if "norm2" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE :      Tuple       =   name.replace("""norm2"""						,    """layernorm_after"""			)
      if "scratch.output_conv" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""scratch.output_conv"""						,    """head"""			)
      if "scratch" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""scratch"""						,    """neck"""			)
      if "layer1_rn" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""layer1_rn"""						,    """convs.0"""			)
      if "layer2_rn" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""layer2_rn"""						,    """convs.1"""			)
      if "layer3_rn" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""layer3_rn"""						,    """convs.2"""			)
      if "layer4_rn" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""layer4_rn"""						,    """convs.3"""			)
      if "refinenet" in name:
            SCREAMING_SNAKE_CASE :      int       =   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
            SCREAMING_SNAKE_CASE :      str       =   name.replace(F"refinenet{layer_idx}"						,    F"fusion_stage.layers.{abs(layer_idx-4			)}"			)
      if "out_conv" in name:
            SCREAMING_SNAKE_CASE :      Tuple       =   name.replace("""out_conv"""						,    """projection"""			)
      if "resConfUnit1" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""resConfUnit1"""						,    """residual_layer1"""			)
      if "resConfUnit2" in name:
            SCREAMING_SNAKE_CASE :      Dict       =   name.replace("""resConfUnit2"""						,    """residual_layer2"""			)
      if "conv1" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""conv1"""						,    """convolution1"""			)
      if "conv2" in name:
            SCREAMING_SNAKE_CASE :      Any       =   name.replace("""conv2"""						,    """convolution2"""			)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            SCREAMING_SNAKE_CASE :      List[Any]       =   name.replace("""pretrained.act_postprocess1.0.project.0"""						,    """neck.reassemble_stage.readout_projects.0.0"""			)
      if "pretrained.act_postprocess2.0.project.0" in name:
            SCREAMING_SNAKE_CASE :      List[Any]       =   name.replace("""pretrained.act_postprocess2.0.project.0"""						,    """neck.reassemble_stage.readout_projects.1.0"""			)
      if "pretrained.act_postprocess3.0.project.0" in name:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   name.replace("""pretrained.act_postprocess3.0.project.0"""						,    """neck.reassemble_stage.readout_projects.2.0"""			)
      if "pretrained.act_postprocess4.0.project.0" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""pretrained.act_postprocess4.0.project.0"""						,    """neck.reassemble_stage.readout_projects.3.0"""			)

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   name.replace("""pretrained.act_postprocess1.3"""						,    """neck.reassemble_stage.layers.0.projection"""			)
      if "pretrained.act_postprocess1.4" in name:
            SCREAMING_SNAKE_CASE :      List[str]       =   name.replace("""pretrained.act_postprocess1.4"""						,    """neck.reassemble_stage.layers.0.resize"""			)
      if "pretrained.act_postprocess2.3" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""pretrained.act_postprocess2.3"""						,    """neck.reassemble_stage.layers.1.projection"""			)
      if "pretrained.act_postprocess2.4" in name:
            SCREAMING_SNAKE_CASE :      Any       =   name.replace("""pretrained.act_postprocess2.4"""						,    """neck.reassemble_stage.layers.1.resize"""			)
      if "pretrained.act_postprocess3.3" in name:
            SCREAMING_SNAKE_CASE :      Any       =   name.replace("""pretrained.act_postprocess3.3"""						,    """neck.reassemble_stage.layers.2.projection"""			)
      if "pretrained.act_postprocess4.3" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""pretrained.act_postprocess4.3"""						,    """neck.reassemble_stage.layers.3.projection"""			)
      if "pretrained.act_postprocess4.4" in name:
            SCREAMING_SNAKE_CASE :      Tuple       =   name.replace("""pretrained.act_postprocess4.4"""						,    """neck.reassemble_stage.layers.3.resize"""			)
      if "pretrained" in name:
            SCREAMING_SNAKE_CASE :      List[Any]       =   name.replace("""pretrained"""						,    """dpt"""			)
      if "bn" in name:
            SCREAMING_SNAKE_CASE :      Any       =   name.replace("""bn"""						,    """batch_norm"""			)
      if "head" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""head"""						,    """head.head"""			)
      if "encoder.norm" in name:
            SCREAMING_SNAKE_CASE :      int       =   name.replace("""encoder.norm"""						,    """layernorm"""			)
      if "auxlayer" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""auxlayer"""						,    """auxiliary_head.head"""			)
      if "backbone" in name:
            SCREAMING_SNAKE_CASE :      Dict       =   name.replace("""backbone"""						,    """backbone.bit.encoder"""			)

      if ".." in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace(""".."""						,    """."""			)

      if "stem.conv" in name:
            SCREAMING_SNAKE_CASE :      List[str]       =   name.replace("""stem.conv"""						,    """bit.embedder.convolution"""			)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE :      Optional[int]       =   name.replace("""blocks"""						,    """layers"""			)
      if "convolution" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""convolution"""						,    """conv"""			)
      if "layer" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   name.replace("""layer"""						,    """layers"""			)
      if "backbone.bit.encoder.bit" in name:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   name.replace("""backbone.bit.encoder.bit"""						,    """backbone.bit"""			)
      if "embedder.conv" in name:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   name.replace("""embedder.conv"""						,    """embedder.convolution"""			)
      if "backbone.bit.encoder.stem.norm" in name:
            SCREAMING_SNAKE_CASE :      str       =   name.replace("""backbone.bit.encoder.stem.norm"""						,    """backbone.bit.embedder.norm"""			)
      return name






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






def 					__lowercase      (     )     ->						Dict:
      SCREAMING_SNAKE_CASE :      Any       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE :      str       =   Image.open(requests.get(_A						,    stream=_A			).raw			)
      return im






@torch.no_grad()
def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A			)     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   get_dpt_config(_A			)
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      SCREAMING_SNAKE_CASE :      Any       =   torch.load(_A						,    map_location="""cpu"""			)
      # remove certain keys
      remove_ignore_keys_(_A			)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE :      List[str]       =   state_dict.pop(_A			)
            SCREAMING_SNAKE_CASE :      Tuple       =   val
      # read in qkv matrices
      read_in_q_k_v(_A						,    _A			)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE :      Tuple       =   DPTForSemanticSegmentation(_A			) if """ade""" in checkpoint_url else DPTForDepthEstimation(_A			)
      model.load_state_dict(_A			)
      model.eval()

      # Check outputs on an image
      SCREAMING_SNAKE_CASE :      int       =   480 if """ade""" in checkpoint_url else 384
      SCREAMING_SNAKE_CASE :      int       =   DPTImageProcessor(size=_A			)

      SCREAMING_SNAKE_CASE :      Any       =   prepare_img()
      SCREAMING_SNAKE_CASE :      List[str]       =   image_processor(_A						,    return_tensors="""pt"""			)

      # forward pass
      SCREAMING_SNAKE_CASE :      Dict       =   model(**_A			).logits if """ade""" in checkpoint_url else model(**_A			).predicted_depth

      if show_prediction:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1			)						,    size=(image.size[1], image.size[0])						,    mode="""bicubic"""						,    align_corners=_A						,    )
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 255			).show()

      if pytorch_dump_folder_path is not None:
            Path(_A			).mkdir(exist_ok=_A			)
            print(F"Saving model to {pytorch_dump_folder_path}"			)
            model.save_pretrained(_A			)
            print(F"Saving image processor to {pytorch_dump_folder_path}"			)
            image_processor.save_pretrained(_A			)

      if push_to_hub:
            model.push_to_hub("""ybelkada/dpt-hybrid-midas"""			)
            image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""			)


if __name__ == "__main__":
  UpperCAmelCase__ :     int				    =    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=False,
      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.""",
  )
  parser.add_argument(
      """--show_prediction""",
      action="""store_true""",
  )

  UpperCAmelCase__ :     Union[str, Any]				    =    parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =["""input_features""", """attention_mask"""]


  def __init__(			self    : List[str]				,				UpperCAmelCase__    : Optional[Any]=8_0				,				UpperCAmelCase__    : List[str]=1_6_0_0_0				,				UpperCAmelCase__    : Optional[int]=8_0				,				UpperCAmelCase__    : List[str]=0.0				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Tuple=True				,				**UpperCAmelCase__    : Optional[int]				,				)					->Optional[int]:
        """simple docstring"""







        super().__init__(feature_size=UpperCAmelCase__				,				sampling_rate=UpperCAmelCase__				,				padding_value=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_mel_bins
        SCREAMING_SNAKE_CASE :      Dict       =   do_ceptral_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   normalize_means
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   normalize_vars
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   True


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : np.ndarray				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        SCREAMING_SNAKE_CASE :      Dict       =   torch.from_numpy(UpperCAmelCase__		).unsqueeze(0		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   ta_kaldi.fbank(UpperCAmelCase__				,				num_mel_bins=self.num_mel_bins				,				sample_frequency=self.sampling_rate		)
        return features.numpy()


  @staticmethod
  def  _lowercase      (			UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : float = 0.0				,				)					->np.ndarray:
        """simple docstring"""







        if normalize_means:
              SCREAMING_SNAKE_CASE :      Tuple       =   x[:input_length].mean(axis=0		)
              SCREAMING_SNAKE_CASE :      List[Any]       =   np.subtract(UpperCAmelCase__				,				UpperCAmelCase__		)
        if normalize_vars:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   x[:input_length].std(axis=0		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.divide(UpperCAmelCase__				,				UpperCAmelCase__		)

        if input_length < x.shape[0]:
              SCREAMING_SNAKE_CASE :      Tuple       =   padding_value

        # make sure array is in float32
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   x.astype(np.floataa		)

        return x


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : Optional[np.ndarray] = None		)					->List[np.ndarray]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(UpperCAmelCase__				,				UpperCAmelCase__				,				self.normalize_means				,				self.normalize_vars				,				self.padding_value		)
            for x, n in zip(UpperCAmelCase__				,				UpperCAmelCase__		)
        ]




  def __call__(			self    : str				,				UpperCAmelCase__    : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				UpperCAmelCase__    : Union[bool, str, PaddingStrategy] = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				**UpperCAmelCase__    : Optional[int]				,				)					->BatchFeature:
        """simple docstring"""







        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                        f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                        f" {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."""		)

        SCREAMING_SNAKE_CASE :      Tuple       =   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}"		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )

        if is_batched:
              SCREAMING_SNAKE_CASE :      Dict       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(UpperCAmelCase__				,				np.ndarray		):
              SCREAMING_SNAKE_CASE :      Any       =   np.asarray(UpperCAmelCase__				,				dtype=np.floataa		)
        elif isinstance(UpperCAmelCase__				,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
              SCREAMING_SNAKE_CASE :      Tuple       =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [raw_speech]

        # extract fbank features
        SCREAMING_SNAKE_CASE :      Tuple       =   [self._extract_fbank_features(UpperCAmelCase__		) for waveform in raw_speech]

        # convert into correct format for padding
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   BatchFeature({"""input_features""": features}		)

        SCREAMING_SNAKE_CASE :      str       =   self.pad(
            UpperCAmelCase__				,				padding=UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				truncation=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        # make sure list is in array format
        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""input_features"""		)
        if isinstance(input_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in input_features]

        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""attention_mask"""		)
        if attention_mask is not None:
              SCREAMING_SNAKE_CASE :      int       =   [np.asarray(UpperCAmelCase__				,				dtype=np.intaa		) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
              SCREAMING_SNAKE_CASE :      Tuple       =   (
                  np.array(UpperCAmelCase__				,				dtype=np.intaa		)
                  if self._get_padding_strategies(UpperCAmelCase__				,				max_length=UpperCAmelCase__		) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              SCREAMING_SNAKE_CASE :      str       =   self.normalize(
                  padded_inputs["""input_features"""]				,				attention_mask=UpperCAmelCase__		)

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE :      Any       =   padded_inputs.convert_to_tensors(UpperCAmelCase__		)

        return padded_inputs




