



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	       =	logging.get_logger(__name__)
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					Any:
   UpperCamelCase   :  List[str]						     =     DPTConfig(embedding_type='hybrid'     )

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

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

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

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

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

   if ".." in name:
      UpperCamelCase   :  List[str]						     =     name.replace('..'	,					'.'     )

   if "stem.conv" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('stem.conv'	,					'bit.embedder.convolution'     )
   if "blocks" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('blocks'	,					'layers'     )
   if "convolution" in name and "backbone" in name:
      UpperCamelCase   :  List[Any]						     =     name.replace('convolution'	,					'conv'     )
   if "layer" in name and "backbone" in name:
      UpperCamelCase   :  str						     =     name.replace('layer'	,					'layers'     )
   if "backbone.bit.encoder.bit" in name:
      UpperCamelCase   :  Dict						     =     name.replace('backbone.bit.encoder.bit'	,					'backbone.bit'     )
   if "embedder.conv" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('embedder.conv'	,					'embedder.convolution'     )
   if "backbone.bit.encoder.stem.norm" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('backbone.bit.encoder.stem.norm'	,					'backbone.bit.embedder.norm'     )
   return name
def 		UpperCamelCase     (       snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Union[str, Any]:
   for i in range(config.num_hidden_layers     ):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      UpperCamelCase   :  List[Any]						     =     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""     )
      UpperCamelCase   :  Optional[int]						     =     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase   :  List[Any]						     =     in_proj_weight[: config.hidden_size, :]
      UpperCamelCase   :  Optional[Any]						     =     in_proj_bias[: config.hidden_size]
      UpperCamelCase   :  List[str]						     =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      UpperCamelCase   :  List[str]						     =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      UpperCamelCase   :  List[Any]						     =     in_proj_weight[
          -config.hidden_size :, :
      ]
      UpperCamelCase   :  Optional[int]						     =     in_proj_bias[-config.hidden_size :]
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  Tuple						     =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase   :  int						     =     Image.open(requests.get(snake_case__	,					stream=snake_case__     ).raw     )
   return im
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any	,					snake_case__	:   Optional[Any]	,					snake_case__	:   Dict	,					snake_case__	:   Any     )     ->					Dict:
   UpperCamelCase     ,					UpperCamelCase   :  Optional[Any]						     =     get_dpt_config(snake_case__     )
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   UpperCamelCase   :  str						     =     torch.load(snake_case__	,					map_location='cpu'     )
   # remove certain keys
   remove_ignore_keys_(snake_case__     )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCamelCase   :  Union[str, Any]						     =     state_dict.pop(snake_case__     )
      UpperCamelCase   :  Any						     =     val
   # read in qkv matrices
   read_in_q_k_v(snake_case__	,					snake_case__     )

   # load HuggingFace model
   UpperCamelCase   :  List[Any]						     =     DPTForSemanticSegmentation(snake_case__     ) if 'ade' in checkpoint_url else DPTForDepthEstimation(snake_case__     )
   model.load_state_dict(snake_case__     )
   model.eval()

   # Check outputs on an image
   UpperCamelCase   :  Tuple						     =     480 if 'ade' in checkpoint_url else 384
   UpperCamelCase   :  str						     =     DPTImageProcessor(size=snake_case__     )

   UpperCamelCase   :  str						     =     prepare_img()
   UpperCamelCase   :  Optional[Any]						     =     image_processor(snake_case__	,					return_tensors='pt'     )

   # forward pass
   UpperCamelCase   :  str						     =     model(**snake_case__     ).logits if 'ade' in checkpoint_url else model(**snake_case__     ).predicted_depth

   if show_prediction:
      UpperCamelCase   :  int						     =     (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1     )	,					size=(image.size[1], image.size[0])	,					mode='bicubic'	,					align_corners=snake_case__	,					)
          .squeeze()
          .cpu()
          .numpy()
      )

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

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

   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	       =	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	       =	parser.parse_args()
    convert_dpt_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
    )








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

import numpy as np

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


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ["input_values", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 1_6000,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 16,      SCREAMING_SNAKE_CASE_ = 64,      SCREAMING_SNAKE_CASE_ = "hann_window",      SCREAMING_SNAKE_CASE_ = 1.0,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 7600,      SCREAMING_SNAKE_CASE_ = 1e-10,      SCREAMING_SNAKE_CASE_ = 2,      SCREAMING_SNAKE_CASE_ = True,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,      sampling_rate=SCREAMING_SNAKE_CASE_,      padding_value=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     return_attention_mask

          UpperCamelCase   :  Union[str, Any]						     =     num_mel_bins
          UpperCamelCase   :  int						     =     hop_length
          UpperCamelCase   :  Any						     =     win_length
          UpperCamelCase   :  Dict						     =     win_function
          UpperCamelCase   :  Any						     =     frame_signal_scale
          UpperCamelCase   :  str						     =     fmin
          UpperCamelCase   :  int						     =     fmax
          UpperCamelCase   :  Dict						     =     mel_floor
          UpperCamelCase   :  Any						     =     reduction_factor

          UpperCamelCase   :  List[str]						     =     win_length * sampling_rate // 1000
          UpperCamelCase   :  Union[str, Any]						     =     hop_length * sampling_rate // 1000
          UpperCamelCase   :  Tuple						     =     optimal_fft_length(self.sample_size	)
          UpperCamelCase   :  int						     =     (self.n_fft // 2) + 1

          UpperCamelCase   :  Any						     =     window_function(window_length=self.sample_size,      name=self.win_function,      periodic=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     mel_filter_bank(
              num_frequency_bins=self.n_freqs,      num_mel_filters=self.num_mel_bins,      min_frequency=self.fmin,      max_frequency=self.fmax,      sampling_rate=self.sampling_rate,      norm='slaney',      mel_scale='slaney',      )

          if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
          if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  snake_case_	(      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 0.0	)					->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase   :  Dict						     =     np.array(SCREAMING_SNAKE_CASE_,      np.intaa	)
             UpperCamelCase   :  int						     =     []

             for vector, length in zip(SCREAMING_SNAKE_CASE_,      attention_mask.sum(-1	)	):
                UpperCamelCase   :  Any						     =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
                if length < normed_slice.shape[0]:
                   UpperCamelCase   :  Optional[Any]						     =     padding_value

                normed_input_values.append(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  str						     =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  int						     =     spectrogram(
              SCREAMING_SNAKE_CASE_,      window=self.window,      frame_length=self.sample_size,      hop_length=self.sample_stride,      fft_length=self.n_fft,      mel_filters=self.mel_filters,      mel_floor=self.mel_floor,      log_mel='log10',      )
          return log_mel_spec.T
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'	)

          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 audio 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.'	)

          if audio is not None:
             UpperCamelCase   :  Dict						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          else:
             UpperCamelCase   :  str						     =     None

          if audio_target is not None:
             UpperCamelCase   :  str						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

             if inputs is None:
                return inputs_target
             else:
                UpperCamelCase   :  Dict						     =     inputs_target['input_values']
                UpperCamelCase   :  str						     =     inputs_target.get('attention_mask'	)
                if decoder_attention_mask is not None:
                   UpperCamelCase   :  str						     =     decoder_attention_mask

          return inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          UpperCamelCase   :  Any						     =     isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and len(speech.shape	) > 1
          if is_batched_numpy and len(speech.shape	) > 2:
             raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
          UpperCamelCase   :  Any						     =     is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,      (list, tuple)	) and (isinstance(speech[0],      (np.ndarray, tuple, list)	))
          )

          if is_batched:
             UpperCamelCase   :  str						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	):
             UpperCamelCase   :  int						     =     np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and speech.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Any						     =     speech.astype(np.floataa	)

          # always return batch
          if not is_batched:
             UpperCamelCase   :  str						     =     [speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase   :  Optional[Any]						     =     self.feature_size

          # convert into correct format for padding
          if is_target:
             UpperCamelCase   :  Optional[int]						     =     [self._extract_mel_features(SCREAMING_SNAKE_CASE_	) for waveform in speech]
             UpperCamelCase   :  Union[str, Any]						     =     BatchFeature({'input_values': features}	)
             UpperCamelCase   :  List[str]						     =     self.num_mel_bins
          else:
             UpperCamelCase   :  Dict						     =     BatchFeature({'input_values': speech}	)

          UpperCamelCase   :  Tuple						     =     self.pad(
              SCREAMING_SNAKE_CASE_,      padding=SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_,      truncation=SCREAMING_SNAKE_CASE_,      pad_to_multiple_of=SCREAMING_SNAKE_CASE_,      return_attention_mask=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  int						     =     feature_size_hack

          # convert input values to correct format
          UpperCamelCase   :  Optional[int]						     =     padded_inputs['input_values']
          if not isinstance(input_values[0],      np.ndarray	):
             UpperCamelCase   :  Union[str, Any]						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)
              and isinstance(input_values[0],      np.ndarray	)
              and input_values[0].dtype is np.dtype(np.floataa	)
          ):
             UpperCamelCase   :  Optional[int]						     =     [array.astype(np.floataa	) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Dict						     =     input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          UpperCamelCase   :  Dict						     =     padded_inputs.get('attention_mask'	)
          if attention_mask is not None:
             UpperCamelCase   :  int						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.intaa	) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
             UpperCamelCase   :  Dict						     =     (
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase   :  Tuple						     =     self.zero_mean_unit_var_norm(
                 padded_inputs['input_values'],      attention_mask=SCREAMING_SNAKE_CASE_,      padding_value=self.padding_value	)

          if return_tensors is not None:
             UpperCamelCase   :  int						     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

          return padded_inputs






       def  snake_case_	(      self	)					->		Dict[str, Any]:
          UpperCamelCase   :  Any						     =     super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase   :  Any						     =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
          for name in names:
             if name in output:
                del output[name]

          return output



