






import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case      :      List[str]             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Any:

						"""simple docstring"""



						print('Loading config file...'						)

						def flatten_yaml_as_dict(UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Dict=""      ,    UpperCamelCase_    :   Optional[Any]="."						):
												lowerCAmelCase__        =  []
												for k, v in d.items():
																		lowerCAmelCase__        =  parent_key + sep + k if parent_key else k
																		if isinstance(UpperCamelCase_      ,    collections.abc.MutableMapping						):
																								items.extend(flatten_yaml_as_dict(UpperCamelCase_      ,    UpperCamelCase_      ,    sep=UpperCamelCase_						).items()						)
																		else:
																								items.append((new_key, v)						)
												return dict(UpperCamelCase_						)

						lowerCAmelCase__        =  argparse.Namespace()
						with open(UpperCamelCase_      ,    'r'						) as yaml_file:
												try:
																		lowerCAmelCase__        =  yaml.load(UpperCamelCase_      ,    Loader=yaml.FullLoader						)

																		lowerCAmelCase__        =  flatten_yaml_as_dict(UpperCamelCase_						)
																		for k, v in flat_cfg.items():
																								setattr(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
												except yaml.YAMLError as exc:
																		logger.error('Error while loading config file: {}. Error message: {}'.format(UpperCamelCase_      ,    str(UpperCamelCase_						)						)						)
						return config







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

						"""simple docstring"""



						lowerCAmelCase__        =  MobileViTVaConfig()

						lowerCAmelCase__        =  False

						# dataset
						if task_name.startswith('imagenet1k_'						):
												lowerCAmelCase__        =  1000
												if int(task_name.strip().split('_'						)[-1]						) == 384:
																		lowerCAmelCase__        =  384
												else:
																		lowerCAmelCase__        =  256
												lowerCAmelCase__        =  'imagenet-1k-id2label.json'
						elif task_name.startswith('imagenet21k_to_1k_'						):
												lowerCAmelCase__        =  2_1000
												if int(task_name.strip().split('_'						)[-1]						) == 384:
																		lowerCAmelCase__        =  384
												else:
																		lowerCAmelCase__        =  256
												lowerCAmelCase__        =  'imagenet-22k-id2label.json'
						elif task_name.startswith('ade20k_'						):
												lowerCAmelCase__        =  151
												lowerCAmelCase__        =  512
												lowerCAmelCase__        =  'ade20k-id2label.json'
												lowerCAmelCase__        =  True
						elif task_name.startswith('voc_'						):
												lowerCAmelCase__        =  21
												lowerCAmelCase__        =  512
												lowerCAmelCase__        =  'pascal-voc-id2label.json'
												lowerCAmelCase__        =  True

						# orig_config
						lowerCAmelCase__        =  load_orig_config_file(UpperCamelCase_						)
						assert getattr(UpperCamelCase_      ,    'model.classification.name'      ,    -1						) == "mobilevit_v2", "Invalid model"
						lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.classification.mitv2.width_multiplier'      ,    1.0						)
						assert (
						    getattr(UpperCamelCase_      ,    'model.classification.mitv2.attn_norm_layer'      ,    -1						) == "layer_norm_2d"
						), "Norm layers other than layer_norm_2d is not supported"
						lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.classification.activation.name'      ,    'swish'						)
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.segmentation.output_stride'      ,    16						)
												if "_deeplabv3" in task_name:
																		lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.segmentation.deeplabv3.aspp_rates'      ,    [12, 24, 36]						)
																		lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.segmentation.deeplabv3.aspp_out_channels'      ,    512						)
																		lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    'model.segmentation.deeplabv3.aspp_dropout'      ,    0.1						)

    # id2label
						lowerCAmelCase__        =  'huggingface/label-files'
						lowerCAmelCase__        =  json.load(open(hf_hub_download(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()}

						return config







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

						"""simple docstring"""



						lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
						lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int=False						)					->				str:

						"""simple docstring"""



						if base_model:
												lowerCAmelCase__        =  ''
						else:
												lowerCAmelCase__        =  'mobilevitv2.'

						lowerCAmelCase__        =  []
						for k in state_dict.keys():
												if k[:8] == "encoder.":
																		lowerCAmelCase__        =  k[8:]
												else:
																		lowerCAmelCase__        =  k

												if ".block." in k:
																		lowerCAmelCase__        =  k_new.replace('.block.'      ,    '.'						)
												if ".conv." in k:
																		lowerCAmelCase__        =  k_new.replace('.conv.'      ,    '.convolution.'						)
												if ".norm." in k:
																		lowerCAmelCase__        =  k_new.replace('.norm.'      ,    '.normalization.'						)

												if "conv_1." in k:
																		lowerCAmelCase__        =  k_new.replace('conv_1.'      ,    F"{model_prefix}conv_stem."						)
												for i in [1, 2]:
																		if F"layer_{i}." in k:
																								lowerCAmelCase__        =  k_new.replace(F"layer_{i}."      ,    F"{model_prefix}encoder.layer.{i-1}.layer."						)
												if ".exp_1x1." in k:
																		lowerCAmelCase__        =  k_new.replace('.exp_1x1.'      ,    '.expand_1x1.'						)
												if ".red_1x1." in k:
																		lowerCAmelCase__        =  k_new.replace('.red_1x1.'      ,    '.reduce_1x1.'						)

												for i in [3, 4, 5]:
																		if F"layer_{i}.0." in k:
																								lowerCAmelCase__        =  k_new.replace(F"layer_{i}.0."      ,    F"{model_prefix}encoder.layer.{i-1}.downsampling_layer."						)
																		if F"layer_{i}.1.local_rep.0." in k:
																								lowerCAmelCase__        =  k_new.replace(F"layer_{i}.1.local_rep.0."      ,    F"{model_prefix}encoder.layer.{i-1}.conv_kxk."						)
																		if F"layer_{i}.1.local_rep.1." in k:
																								lowerCAmelCase__        =  k_new.replace(F"layer_{i}.1.local_rep.1."      ,    F"{model_prefix}encoder.layer.{i-1}.conv_1x1."						)

												for i in [3, 4, 5]:
																		if i == 3:
																								lowerCAmelCase__        =  [0, 1]
																		elif i == 4:
																								lowerCAmelCase__        =  [0, 1, 2, 3]
																		elif i == 5:
																								lowerCAmelCase__        =  [0, 1, 2]

																		for j in j_in:
																								if F"layer_{i}.1.global_rep.{j}." in k:
																														lowerCAmelCase__        =  k_new.replace(
																														    F"layer_{i}.1.global_rep.{j}."      ,    F"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."						)
																		if F"layer_{i}.1.global_rep.{j+1}." in k:
																								lowerCAmelCase__        =  k_new.replace(
																								    F"layer_{i}.1.global_rep.{j+1}."      ,    F"{model_prefix}encoder.layer.{i-1}.layernorm."						)

																		if F"layer_{i}.1.conv_proj." in k:
																								lowerCAmelCase__        =  k_new.replace(F"layer_{i}.1.conv_proj."      ,    F"{model_prefix}encoder.layer.{i-1}.conv_projection."						)

												if "pre_norm_attn.0." in k:
																		lowerCAmelCase__        =  k_new.replace('pre_norm_attn.0.'      ,    'layernorm_before.'						)
												if "pre_norm_attn.1." in k:
																		lowerCAmelCase__        =  k_new.replace('pre_norm_attn.1.'      ,    'attention.'						)
												if "pre_norm_ffn.0." in k:
																		lowerCAmelCase__        =  k_new.replace('pre_norm_ffn.0.'      ,    'layernorm_after.'						)
												if "pre_norm_ffn.1." in k:
																		lowerCAmelCase__        =  k_new.replace('pre_norm_ffn.1.'      ,    'ffn.conv1.'						)
												if "pre_norm_ffn.3." in k:
																		lowerCAmelCase__        =  k_new.replace('pre_norm_ffn.3.'      ,    'ffn.conv2.'						)

												if "classifier.1." in k:
																		lowerCAmelCase__        =  k_new.replace('classifier.1.'      ,    'classifier.'						)

												if "seg_head." in k:
																		lowerCAmelCase__        =  k_new.replace('seg_head.'      ,    'segmentation_head.'						)
												if ".aspp_layer." in k:
																		lowerCAmelCase__        =  k_new.replace('.aspp_layer.'      ,    '.'						)
												if ".aspp_pool." in k:
																		lowerCAmelCase__        =  k_new.replace('.aspp_pool.'      ,    '.'						)

												rename_keys.append((k, k_new)						)
						return rename_keys







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						for k in state_dict.keys():
												if k.startswith('seg_head.aux_head.'						):
																		keys_to_ignore.append(UpperCamelCase_						)
						for k in keys_to_ignore:
												state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      )					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
						lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
						return im







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

						"""simple docstring"""



						lowerCAmelCase__        =  get_mobilevitva_config(UpperCamelCase_      ,    UpperCamelCase_						)

						# load original state_dict
						lowerCAmelCase__        =  torch.load(UpperCamelCase_      ,    map_location='cpu'						)

						# load huggingface model
						if task_name.startswith('ade20k_'						) or task_name.startswith('voc_'						):
												lowerCAmelCase__        =  MobileViTVaForSemanticSegmentation(UpperCamelCase_						).eval()
												lowerCAmelCase__        =  False
						else:
												lowerCAmelCase__        =  MobileViTVaForImageClassification(UpperCamelCase_						).eval()
												lowerCAmelCase__        =  False

						# remove and rename some keys of load the original model
						lowerCAmelCase__        =  checkpoint
						remove_unused_keys(UpperCamelCase_						)
						lowerCAmelCase__        =  create_rename_keys(UpperCamelCase_      ,    base_model=UpperCamelCase_						)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

						# load modified state_dict
						model.load_state_dict(UpperCamelCase_						)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						lowerCAmelCase__        =  MobileViTImageProcessor(crop_size=config.image_size      ,    size=config.image_size + 32						)
						lowerCAmelCase__        =  image_processor(images=prepare_img()      ,    return_tensors='pt'						)
						lowerCAmelCase__        =  model(**UpperCamelCase_						)

						# verify classification model
						if task_name.startswith('imagenet'						):
												lowerCAmelCase__        =  outputs.logits
												lowerCAmelCase__        =  logits.argmax(-1						).item()
												print('Predicted class:'      ,    model.config.idalabel[predicted_class_idx]						)
												if task_name.startswith('imagenet1k_256'						) and config.width_multiplier == 1.0:
																		# expected_logits for base variant
																		lowerCAmelCase__        =  torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						)
																		assert torch.allclose(logits[0, :3]      ,    UpperCamelCase_      ,    atol=1e-4						)

						Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
						print(F"Saving model {task_name} 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 __name__ == "__main__":
					__snake_case      :      List[Any]             =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""",
					    default="""imagenet1k_256""",
					    type=str,
					    help=(
					        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
					        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
					    ),
					    choices=[
					        """imagenet1k_256""",
					        """imagenet1k_384""",
					        """imagenet21k_to_1k_256""",
					        """imagenet21k_to_1k_384""",
					        """ade20k_deeplabv3""",
					        """voc_deeplabv3""",
					    ],
					)

					parser.add_argument(
					    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)

					__snake_case      :      Dict             =       parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)













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





