




import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


__magic_name__  :  Tuple					=       logging.getLogger(__name__)

__magic_name__  :  Any					=       """pytorch_model.bin"""





@dataclasses.dataclass
class     SCREAMING_SNAKE_CASE__		:
     lowercase_ :      str				= dataclasses.field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}			)
     lowercase_ :      Optional[str]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}    ,					)





@dataclasses.dataclass
class     SCREAMING_SNAKE_CASE__		:
     lowercase_ :      str				= dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}			)
     lowercase_ :      str				= dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}			)
     lowercase_ :      Optional[str]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "A csv or a json file containing the validation data."}			)
     lowercase_ :      Optional[str]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "The name of the task to train on."}    ,					)
     lowercase_ :      Optional[List[str]]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "The list of labels for the task."}			)





@dataclasses.dataclass
class     SCREAMING_SNAKE_CASE__		:
     lowercase_ :      str				= dataclasses.field(
         metadata={"help": "The output directory where the model predictions and checkpoints will be written."}			)
     lowercase_ :      Optional[str]				= dataclasses.field(
         default="accuracy"    ,					metadata={"help": "The evaluation metric used for the task."}			)
     lowercase_ :      Optional[str]				= dataclasses.field(
         default="no"    ,					metadata={
             "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
         }    ,					)
     lowercase_ :      Optional[int]				= dataclasses.field(
         default=10    ,					metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}    ,					)
     lowercase_ :      Optional[float]				= dataclasses.field(
         default=0.0    ,					metadata={
             "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
         }    ,					)
     lowercase_ :      Optional[bool]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}    ,					)
     lowercase_ :      Optional[bool]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}    ,					)
     lowercase_ :      Optional[bool]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}    ,					)
     lowercase_ :      Optional[float]				= dataclasses.field(
         default=0.0    ,					metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}    ,					)
     lowercase_ :      Optional[int]				= dataclasses.field(
         default=100    ,					metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}    ,					)
     lowercase_ :      Optional[int]				= dataclasses.field(
         default=UpperCAmelCase_    ,					metadata={"help": "Random seed for initialization."}    ,					)



def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      datasets.concatenate_datasets([infer_input, infer_output]   , axis=1				)

  if args.do_filter_by_confidence:
    lowerCAmelCase__										=      dataset.filter(lambda __lowerCAmelCase				: example["probability"] > args.confidence_threshold				)

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    lowerCAmelCase__										=      int(eval_result * len(__UpperCamelCase				)				)
    print(__UpperCamelCase				)
    lowerCAmelCase__										=      dataset.sort('''probability'''   , reverse=__UpperCamelCase				)
    lowerCAmelCase__										=      dataset.select(range(__UpperCamelCase				)				)

  lowerCAmelCase__										=      dataset.remove_columns(['''label''', '''probability''']				)
  lowerCAmelCase__										=      dataset.rename_column('''prediction'''   , '''label'''				)
  lowerCAmelCase__										=      dataset.map(lambda __lowerCAmelCase				: {"label": idalabel[example["label"]]}				)
  lowerCAmelCase__										=      dataset.shuffle(seed=args.seed				)

  lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , F"""train_pseudo.{args.data_file_extension}"""				)
  if args.data_file_extension == "csv":
    dataset.to_csv(__UpperCamelCase   , index=__UpperCamelCase				)
  else:
    dataset.to_json(__UpperCamelCase				)




def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , **__lowerCAmelCase				):
  lowerCAmelCase__										=      Accelerator()
  # Make one log on every process with the configuration for debugging.
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   , datefmt='''%m/%d/%Y %H:%M:%S'''   , level=logging.INFO   , )
  logger.info(accelerator.state				)

  # Setup logging, we only want one process per machine to log things on the
  # screen. accelerator.is_local_main_process is only True for one process per
  # machine.
  logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR				)

  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()

  lowerCAmelCase__										=      STModelArguments(model_name_or_path=__UpperCamelCase				)
  lowerCAmelCase__										=      STDataArguments(train_file=__UpperCamelCase   , infer_file=__UpperCamelCase				)
  lowerCAmelCase__										=      STTrainingArguments(output_dir=__UpperCamelCase				)
  lowerCAmelCase__										=      argparse.Namespace()

  for arg_class in (model_args, data_args, training_args):
    for key, value in vars(__UpperCamelCase				).items():
      setattr(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase				)

  for key, value in kwargs.items():
    if hasattr(__UpperCamelCase   , __UpperCamelCase				):
      setattr(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase				)

    # Sanity checks
  lowerCAmelCase__										=      {}
  lowerCAmelCase__										=      None

  # You need to provide the training data and the data to predict on
  assert args.train_file is not None
  assert args.infer_file is not None
  lowerCAmelCase__										=      args.train_file
  lowerCAmelCase__										=      args.infer_file

  if args.evaluation_strategy != IntervalStrategy.NO.value:
    assert args.eval_file is not None
    lowerCAmelCase__										=      args.eval_file

  for key in data_files:
    lowerCAmelCase__										=      data_files[key].split('''.'''				)[-1]
    assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
    if args.data_file_extension is None:
      lowerCAmelCase__										=      extension
    else:
      assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

  assert (
      args.eval_metric in datasets.list_metrics()
  ), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

  # If passed along, set the training seed now.
  if args.seed is not None:
    set_seed(args.seed				)

  logger.info('''Creating the initial data directory for self-training...'''				)
  lowerCAmelCase__										=      F"""{args.output_dir}/self-train_iter-{{}}""".format
  lowerCAmelCase__										=      data_dir_format(0				)

  if accelerator.is_main_process:
    if args.output_dir is not None:
      os.makedirs(args.output_dir   , exist_ok=__UpperCamelCase				)
      os.makedirs(__UpperCamelCase   , exist_ok=__UpperCamelCase				)
  accelerator.wait_for_everyone()

  lowerCAmelCase__										=      None
  lowerCAmelCase__										=      None
  lowerCAmelCase__										=      0
  lowerCAmelCase__										=      False
  # Show the progress bar
  lowerCAmelCase__										=      tqdm(range(args.max_selftrain_iterations				)   , disable=not accelerator.is_local_main_process				)

  # Self-train
  for iteration in range(0   , int(args.max_selftrain_iterations				)				):
    lowerCAmelCase__										=      data_dir_format(__UpperCamelCase				)
    assert os.path.exists(__UpperCamelCase				)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''stage-1'''				)
    lowerCAmelCase__										=      {
        '''accelerator''': accelerator,
        '''model_name_or_path''': args.model_name_or_path,
        '''cache_dir''': args.cache_dir,
        '''do_train''': True,
        '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
        '''do_eval''': True if args.eval_file is not None else False,
        '''eval_file''': data_files['''eval'''],
        '''do_predict''': True,
        '''infer_file''': data_files['''infer'''],
        '''task_name''': args.task_name,
        '''label_list''': args.label_list,
        '''output_dir''': current_output_dir,
        '''eval_metric''': args.eval_metric,
        '''evaluation_strategy''': args.evaluation_strategy,
        '''early_stopping_patience''': args.early_stopping_patience,
        '''early_stopping_threshold''': args.early_stopping_threshold,
        '''seed''': args.seed,
    }
    # Add additional training arguments
    for key, value in kwargs.items():
      if key not in arguments_dict and not hasattr(__UpperCamelCase   , __UpperCamelCase				):
        arguments_dict.update({key: value}				)

    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''best-checkpoint'''   , __UpperCamelCase				)
    if os.path.exists(__UpperCamelCase				):
      logger.info(
          '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''   , __UpperCamelCase   , __UpperCamelCase   , )
    else:
      logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''   , __UpperCamelCase				)
      finetune(**__UpperCamelCase				)
      accelerator.wait_for_everyone()
      assert os.path.exists(__UpperCamelCase				)
      logger.info('''Self-training job completed: iteration: %d, stage: 1.'''   , __UpperCamelCase				)

    if iteration > 0 and args.finetune_on_labeled_data:
      # Stage 2 (optional): fine-tuning on the original labeled data
      lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''best-checkpoint'''				)
      lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''stage-2'''				)
      # Update arguments_dict
      lowerCAmelCase__										=      model_path
      lowerCAmelCase__										=      data_files['''train''']
      lowerCAmelCase__										=      current_output_dir

      lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''best-checkpoint'''   , __UpperCamelCase				)
      if os.path.exists(__UpperCamelCase				):
        logger.info(
            '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''   , __UpperCamelCase   , __UpperCamelCase   , )
      else:
        logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''   , __UpperCamelCase				)
        finetune(**__UpperCamelCase				)
        accelerator.wait_for_everyone()
        assert os.path.exists(__UpperCamelCase				)
        logger.info('''Self-training job completed: iteration: %d, stage: 2.'''   , __UpperCamelCase				)

    lowerCAmelCase__										=      iteration
    lowerCAmelCase__										=      data_dir_format(iteration + 1				)

    lowerCAmelCase__										=      AutoConfig.from_pretrained(os.path.join(__UpperCamelCase   , '''best-checkpoint'''				)				)
    lowerCAmelCase__										=      config.idalabel
    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''eval_results_best-checkpoint.json'''				)
    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''test_results_best-checkpoint.json'''				)
    assert os.path.exists(__UpperCamelCase				)

    with open(__UpperCamelCase   , '''r'''				) as f:
      lowerCAmelCase__										=      float(json.load(__UpperCamelCase				)[args.eval_metric]				)
    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , '''infer_output_best-checkpoint.csv'''				)
    assert os.path.exists(__UpperCamelCase				)
    # Loading the dataset from local csv or json files.
    lowerCAmelCase__										=      load_dataset(args.data_file_extension   , data_files={'''data''': data_files['''infer''']}				)['''data''']
    lowerCAmelCase__										=      load_dataset('''csv'''   , data_files={'''data''': infer_output_file}				)['''data''']

    if accelerator.is_main_process:
      os.makedirs(__UpperCamelCase   , exist_ok=__UpperCamelCase				)
      shutil.copy(__UpperCamelCase   , os.path.join(__UpperCamelCase   , F"""eval_results_iter-{iteration}.json"""				)				)
      if os.path.exists(__UpperCamelCase				):
        shutil.copy(__UpperCamelCase   , os.path.join(__UpperCamelCase   , F"""test_results_iter-{iteration}.json"""				)				)
      create_pseudo_labeled_data(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase				)
    accelerator.wait_for_everyone()

    lowerCAmelCase__										=      os.path.join(__UpperCamelCase   , F"""train_pseudo.{args.data_file_extension}"""				)

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      lowerCAmelCase__										=      eval_result

      if best_iteration is None:
        lowerCAmelCase__										=      new_iteration
        lowerCAmelCase__										=      new_eval_result
      else:
        if new_eval_result - best_eval_result > args.early_stopping_threshold:
          lowerCAmelCase__										=      new_iteration
          lowerCAmelCase__										=      new_eval_result
          lowerCAmelCase__										=      0
        else:
          if new_eval_result == best_eval_result:
            lowerCAmelCase__										=      new_iteration
            lowerCAmelCase__										=      new_eval_result
          early_stopping_patience_counter += 1

        if early_stopping_patience_counter >= args.early_stopping_patience:
          lowerCAmelCase__										=      True

    progress_bar.update(1				)

    if should_training_stop:
      break

  if best_iteration is not None:
    # Save the best iteration
    logger.info('''Best iteration: %d'''   , __UpperCamelCase				)
    logger.info('''Best evaluation result: %s = %f'''   , args.eval_metric   , __UpperCamelCase				)
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(__UpperCamelCase   , F"""eval_results_iter-{iteration}.json"""				)   , os.path.join(__UpperCamelCase   , '''eval_results_best-iteration.json'''				)   , )
  else:
    # Assume that the last iteration is the best
    logger.info('''Best iteration: %d'''   , args.max_selftrain_iterations - 1				)
    logger.info('''Best evaluation result: %s = %f'''   , args.eval_metric   , __UpperCamelCase				)
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(__UpperCamelCase   , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""				)   , os.path.join(__UpperCamelCase   , '''eval_results_best-iteration.json'''				)   , )






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


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     ["input_values", "attention_mask"]



					def __init__(  self       :							str					,       _snake_case       :							int = 1					,       _snake_case       :							int = 1_60_00					,       _snake_case       :							float = 0.0					,       _snake_case       :							bool = False					,       _snake_case       :							int = 80					,       _snake_case       :							int = 16					,       _snake_case       :							int = 64					,       _snake_case       :							str = "hann_window"					,       _snake_case       :							float = 1.0					,       _snake_case       :							float = 80					,       _snake_case       :							float = 76_00					,       _snake_case       :							float = 1E-10					,       _snake_case       :							int = 2					,       _snake_case       :							bool = True					,       **_snake_case       :							Union[str, Any]					,       ):






								"""simple docstring"""


								super().__init__(feature_size=_snake_case					,       sampling_rate=_snake_case					,       padding_value=_snake_case					,       **_snake_case       )
								A__	  =     do_normalize
								A__	  =     return_attention_mask

								A__	  =     num_mel_bins
								A__	  =     hop_length
								A__	  =     win_length
								A__	  =     win_function
								A__	  =     frame_signal_scale
								A__	  =     fmin
								A__	  =     fmax
								A__	  =     mel_floor
								A__	  =     reduction_factor

								A__	  =     win_length * sampling_rate // 10_00
								A__	  =     hop_length * sampling_rate // 10_00
								A__	  =     optimal_fft_length(self.sample_size       )
								A__	  =     (self.n_fft // 2) + 1

								A__	  =     window_function(window_length=self.sample_size					,       name=self.win_function					,       periodic=_snake_case       )

								A__	  =     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'					,       _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'					,       _snake_case					,       )



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _a				(  _snake_case       :							List[np.ndarray]					,       _snake_case       :							List[np.ndarray]					,       _snake_case       :							float = 0.0       ):






								"""simple docstring"""


								if attention_mask is not None:
											A__	  =     np.array(_snake_case					,       np.intaa       )
											A__	  =     []

											for vector, length in zip(_snake_case					,       attention_mask.sum(-1       )       ):
														A__	  =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
														if length < normed_slice.shape[0]:
																	A__	  =     padding_value

														normed_input_values.append(_snake_case       )
								else:
											A__	  =     [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

								return normed_input_values



					def      _a				(  self       :							Tuple					,       _snake_case       :							np.ndarray					,       ):






								"""simple docstring"""


								A__	  =     spectrogram(
								    _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       :							List[str]					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[int] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								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:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )
								else:
											A__	  =     None

								if audio_target is not None:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )

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

								return inputs



					def      _a				(  self       :							Tuple					,       _snake_case       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,       _snake_case       :							bool = False					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								A__	  =     isinstance(_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}'''       )
								A__	  =     is_batched_numpy or (
								    isinstance(_snake_case					,       (list, tuple)       ) and (isinstance(speech[0]					,       (np.ndarray, tuple, list)       ))
								)

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

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

								# needed to make pad() work on spectrogram inputs
								A__	  =     self.feature_size

								# convert into correct format for padding
								if is_target:
											A__	  =     [self._extract_mel_features(_snake_case       ) for waveform in speech]
											A__	  =     BatchFeature({'input_values': features}       )
											A__	  =     self.num_mel_bins
								else:
											A__	  =     BatchFeature({'input_values': speech}       )

								A__	  =     self.pad(
								    _snake_case					,       padding=_snake_case					,       max_length=_snake_case					,       truncation=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=_snake_case					,       **_snake_case					,       )

								A__	  =     feature_size_hack

								# convert input values to correct format
								A__	  =     padded_inputs['input_values']
								if not isinstance(input_values[0]					,       np.ndarray       ):
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for array in input_values]
								elif (
								    not isinstance(_snake_case					,       np.ndarray       )
								    and isinstance(input_values[0]					,       np.ndarray       )
								    and input_values[0].dtype is np.dtype(np.floataa       )
								):
											A__	  =     [array.astype(np.floataa       ) for array in input_values]
								elif isinstance(_snake_case					,       np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
											A__	  =     input_values.astype(np.floataa       )

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

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

								if return_tensors is not None:
											A__	  =     padded_inputs.convert_to_tensors(_snake_case       )

								return padded_inputs



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     super().to_dict()

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

								return output




