






# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


__magic_name__   :  int        =							float('''nan''')





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  sys.stdout
           UpperCamelCase					       =  open(_SCREAMING_SNAKE_CASE   ,						'a' )







     def __getattr__( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Tuple ):






           """simple docstring"""




           return getattr(self.stdout   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] ):






           """simple docstring"""




           self.stdout.write(_SCREAMING_SNAKE_CASE )
           # strip tqdm codes
           self.file.write(re.sub(r'^.*\r'   ,						''   ,						_SCREAMING_SNAKE_CASE   ,						0   ,						re.M ) )

def 		lowercase__  (				_UpperCamelCase=80       ,							_UpperCamelCase=False) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  []

      # deal with critical env vars
      UpperCamelCase					       =  ["CUDA_VISIBLE_DEVICES"]
      for key in env_keys:
            UpperCamelCase					       =  os.environ.get(_UpperCAmelCase       ,							_UpperCAmelCase)
            if val is not None:
                  cmd.append(F'{key}={val}')

    # python executable (not always needed if the script is executable)
      UpperCamelCase					       =  sys.executable if full_python_path else sys.executable.split('/')[-1]
      cmd.append(_UpperCAmelCase)

      # now the normal args
      cmd += list(map(shlex.quote       ,							sys.argv))

      # split up into up to MAX_WIDTH lines with shell multi-line escapes
      UpperCamelCase					       =  []
      UpperCamelCase					       =  ""
      while len(_UpperCAmelCase) > 0:
            current_line += F'{cmd.pop(0)} '
            if len(_UpperCAmelCase) == 0 or len(_UpperCAmelCase) + len(cmd[0]) + 1 > max_width - 1:
                  lines.append(_UpperCAmelCase)
                  UpperCamelCase					       =  ""
      return "\\\n".join(_UpperCAmelCase)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  re.sub(r'[\\\n]+'       ,							' '       ,							args.base_cmd)

      # remove --output_dir if any and set our own
      UpperCamelCase					       =  re.sub('--output_dir\s+[^\s]+'       ,							''       ,							args.base_cmd)
      args.base_cmd += F' --output_dir {output_dir}'

      # ensure we have --overwrite_output_dir
      UpperCamelCase					       =  re.sub('--overwrite_output_dir\s+'       ,							''       ,							args.base_cmd)
      args.base_cmd += " --overwrite_output_dir"

      return [sys.executable] + shlex.split(args.base_cmd)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Dict:
      """simple docstring"""

      if 0:
            import random
            from time import sleep

            sleep(0)
            return dict(
                {k: random.uniform(0       ,							1_00) for k in metric_keys}       ,							**{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2])}       ,							)

      UpperCamelCase					       =  subprocess.run(_UpperCAmelCase       ,							capture_output=_UpperCAmelCase       ,							text=_UpperCAmelCase)

      if verbose:
            print('STDOUT'       ,							result.stdout)
            print('STDERR'       ,							result.stderr)

      # save the streams
      UpperCamelCase					       =  variation.replace(' '       ,							'-')
      with open(Path(_UpperCAmelCase) / F'log.{prefix}.stdout.txt'       ,							'w') as f:
            f.write(result.stdout)
      with open(Path(_UpperCAmelCase) / F'log.{prefix}.stderr.txt'       ,							'w') as f:
            f.write(result.stderr)

      if result.returncode != 0:
            if verbose:
                  print('failed')
            return {target_metric_key: nan}

      with io.open(F'{output_dir}/all_results.json'       ,							'r'       ,							encoding='utf-8') as f:
            UpperCamelCase					       =  json.load(_UpperCAmelCase)

      # filter out just the keys we want
      return {k: v for k, v in metrics.items() if k in metric_keys}
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  F'{id}: {variation:<{longest_variation_len}}'
      UpperCamelCase					       =  F'{preamble}: '
      UpperCamelCase					       =  set(report_metric_keys + [target_metric_key])
      for i in tqdm(range(_UpperCAmelCase)       ,							desc=_UpperCAmelCase       ,							leave=_UpperCAmelCase):
            UpperCamelCase					       =  process_run_single(
                _UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase)
            UpperCamelCase					       =  single_run_metrics[target_metric_key]
            if not math.isnan(_UpperCAmelCase):
                  metrics.append(_UpperCAmelCase)
                  results.append(_UpperCAmelCase)
                  outcome += "✓"
            else:
                  outcome += "✘"
      UpperCamelCase					       =  F'\33[2K\r{outcome}'
      if len(_UpperCAmelCase) > 0:
            UpperCamelCase					       =  {k: fmean([x[k] for x in metrics]) for k in metrics[0].keys()}
            UpperCamelCase					       =  round(mean_metrics[target_metric_key]       ,							2)
            UpperCamelCase					       =  F'{outcome} {mean_target}'
            if len(_UpperCAmelCase) > 1:
                  results_str += F' {tuple(round(_UpperCAmelCase       ,							2) for x in results)}'
            print(_UpperCAmelCase)
            UpperCamelCase					       =  variation
            return mean_metrics
      else:
            print(_UpperCAmelCase)
            return {variation_key: variation, target_metric_key: nan}
def 		lowercase__  (				) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  torch.cuda.get_device_properties(torch.device('cuda'))
      return F'\nDatetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  pd.DataFrame(_UpperCAmelCase)
      UpperCamelCase					       =  "variation"
      UpperCamelCase					       =  "diff_%"

      UpperCamelCase					       =  nan
      if base_variation is not None and len(df[df[variation_key] == base_variation]):
            # this may still return nan
            UpperCamelCase					       =  df.loc[df[variation_key] == base_variation][target_metric_key].item()
      if math.isnan(_UpperCAmelCase):
            # as a fallback, use the minimal value as the sentinel
            UpperCamelCase					       =  df.loc[df[target_metric_key] != nan][target_metric_key].min()

      # create diff column if possible
      if not math.isnan(_UpperCAmelCase):
            UpperCamelCase					       =  df.apply(
                lambda _UpperCamelCase: round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value)
                if not math.isnan(r[target_metric_key])
                else 0       ,							axis='columns'       ,							)

      # re-order columns
      UpperCamelCase					       =  [variation_key, target_metric_key, diff_key, *report_metric_keys]
      UpperCamelCase					       =  df.reindex(_UpperCAmelCase       ,							axis='columns')  # reorder cols

      # capitalize
      UpperCamelCase					       =  df.rename(str.capitalize       ,							axis='columns')

      # make the cols as narrow as possible
      UpperCamelCase					       =  df.rename(lambda _UpperCamelCase: c.replace('_'       ,							'<br>')       ,							axis='columns')
      UpperCamelCase					       =  df.rename(lambda _UpperCamelCase: c.replace('_'       ,							'\n')       ,							axis='columns')

      UpperCamelCase					       =  ["", "Copy between the cut-here-lines and paste as is to github or a forum"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results:", df_github.to_markdown(index=_UpperCAmelCase       ,							floatfmt='.2f')]
      report += ["```"]
      report += ["*** Setup:", get_versions()]
      report += ["*** The benchmark command line was:", get_original_command()]
      report += ["```"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results (console):", df_console.to_markdown(index=_UpperCAmelCase       ,							floatfmt='.2f')]

      print('\n\n'.join(_UpperCAmelCase))
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  argparse.ArgumentParser()
      parser.add_argument(
          '--base-cmd'       ,							default=_UpperCAmelCase       ,							type=_UpperCAmelCase       ,							required=_UpperCAmelCase       ,							help='Base cmd'       ,							)
      parser.add_argument(
          '--variations'       ,							default=_UpperCAmelCase       ,							type=_UpperCAmelCase       ,							nargs='+'       ,							required=_UpperCAmelCase       ,							help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''       ,							)
      parser.add_argument(
          '--base-variation'       ,							default=_UpperCAmelCase       ,							type=_UpperCAmelCase       ,							help='Baseline variation to compare to. if None the minimal target value will be used to compare against'       ,							)
      parser.add_argument(
          '--target-metric-key'       ,							default=_UpperCAmelCase       ,							type=_UpperCAmelCase       ,							required=_UpperCAmelCase       ,							help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'       ,							)
      parser.add_argument(
          '--report-metric-keys'       ,							default=''       ,							type=_UpperCAmelCase       ,							help='Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples'       ,							)
      parser.add_argument(
          '--repeat-times'       ,							default=1       ,							type=_UpperCAmelCase       ,							help='How many times to re-run each variation - an average will be reported'       ,							)
      parser.add_argument(
          '--output_dir'       ,							default='output_benchmark'       ,							type=_UpperCAmelCase       ,							help='The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked'       ,							)
      parser.add_argument(
          '--verbose'       ,							default=_UpperCAmelCase       ,							action='store_true'       ,							help='Whether to show the outputs of each run or just the benchmark progress'       ,							)
      UpperCamelCase					       =  parser.parse_args()

      UpperCamelCase					       =  args.output_dir
      Path(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
      UpperCamelCase					       =  get_base_command(_UpperCAmelCase       ,							_UpperCAmelCase)

      # split each dimension into its --foo variations
      UpperCamelCase					       =  [list(map(str.strip       ,							re.split(r'\|'       ,							_UpperCAmelCase))) for x in args.variations]
      # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
      # while stripping white space for inputs that were empty
      UpperCamelCase					       =  list(map(str.strip       ,							map(' '.join       ,							itertools.product(*_UpperCAmelCase))))
      UpperCamelCase					       =  max(len(_UpperCAmelCase) for x in variations)

      # split wanted keys
      UpperCamelCase					       =  args.report_metric_keys.split()

      # capture prints into a log file for convenience
      UpperCamelCase					       =  F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.txt'
      print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt')
      print(F'and this script\'s output is also piped into {report_fn}')

      UpperCamelCase					       =  Tee(_UpperCAmelCase)

      print(F'\n*** Running {len(_UpperCAmelCase)} benchmarks:')
      print(F'Base command: {" ".join(_UpperCAmelCase)}')

      UpperCamelCase					       =  "variation"
      UpperCamelCase					       =  []
      for id, variation in enumerate(tqdm(_UpperCAmelCase       ,							desc='Total completion: '       ,							leave=_UpperCAmelCase)):
            UpperCamelCase					       =  base_cmd + variation.split()
            results.append(
                process_run(
                    id + 1       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							args.target_metric_key       ,							_UpperCAmelCase       ,							args.repeat_times       ,							_UpperCAmelCase       ,							args.verbose       ,							))

      process_results(_UpperCAmelCase       ,							args.target_metric_key       ,							_UpperCAmelCase       ,							args.base_variation       ,							_UpperCAmelCase)


if __name__ == "__main__":
       main()





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_						:     Union[str, Any]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["input_values", "attention_mask"]






	def __init__(self				: int      ,	snake_case				: int = 1      ,	snake_case				: int = 1_6000      ,	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 = 7600      ,	snake_case				: float = 1e-10      ,	snake_case				: int = 2      ,	snake_case				: bool = True      ,	**snake_case				: Any      ,	) ->			str:
							super().__init__(feature_size=snake_case      ,	sampling_rate=snake_case      ,	padding_value=snake_case      ,	**snake_case    )
							_lowercase       :	int           =   do_normalize
							_lowercase       :	Any           =   return_attention_mask

							_lowercase       :	str           =   num_mel_bins
							_lowercase       :	str           =   hop_length
							_lowercase       :	List[Any]           =   win_length
							_lowercase       :	Tuple           =   win_function
							_lowercase       :	Any           =   frame_signal_scale
							_lowercase       :	Any           =   fmin
							_lowercase       :	Any           =   fmax
							_lowercase       :	List[str]           =   mel_floor
							_lowercase       :	str           =   reduction_factor

							_lowercase       :	str           =   win_length * sampling_rate // 1000
							_lowercase       :	Optional[int]           =   hop_length * sampling_rate // 1000
							_lowercase       :	Tuple           =   optimal_fft_length(self.sample_size    )
							_lowercase       :	int           =   (self.n_fft // 2) + 1

							_lowercase       :	Tuple           =   window_function(window_length=self.sample_size      ,	name=self.win_function      ,	periodic=snake_case    )

							_lowercase       :	Optional[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"      ,	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    ) ->			List[np.ndarray]:
							if attention_mask is not None:
													_lowercase       :	List[str]           =   np.array(snake_case      ,	np.intaa    )
													_lowercase       :	Dict           =   []

													for vector, length in zip(snake_case      ,	attention_mask.sum(-1    )    ):
																			_lowercase       :	Tuple           =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
																			if length < normed_slice.shape[0]:
																									_lowercase       :	List[str]           =   padding_value

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

							return normed_input_values






	def       _a(self				: Optional[Any]      ,	snake_case				: np.ndarray      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   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				: Tuple      ,	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				: Union[str, Any]      ,	) ->			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:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)
							else:
													_lowercase       :	str           =   None

							if audio_target is not None:
													_lowercase       :	Union[str, Any]           =   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:
																			_lowercase       :	int           =   inputs_target["input_values"]
																			_lowercase       :	Optional[int]           =   inputs_target.get("attention_mask"    )
																			if decoder_attention_mask is not None:
																									_lowercase       :	List[str]           =   decoder_attention_mask

							return inputs






	def       _a(self				: Optional[Any]      ,	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				: int      ,	) ->			BatchFeature:
							_lowercase       :	int           =   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}"""    )
							_lowercase       :	List[str]           =   is_batched_numpy or (
							    isinstance(snake_case      ,	(list, tuple)    ) and (isinstance(speech[0]      ,	(np.ndarray, tuple, list)    ))
							)

							if is_batched:
													_lowercase       :	List[Any]           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for speech in speech]
							elif not is_batched and not isinstance(snake_case      ,	np.ndarray    ):
													_lowercase       :	Optional[Any]           =   np.asarray(snake_case      ,	dtype=np.floataa    )
							elif isinstance(snake_case      ,	np.ndarray    ) and speech.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Optional[int]           =   speech.astype(np.floataa    )

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

							# needed to make pad() work on spectrogram inputs
							_lowercase       :	Tuple           =   self.feature_size

							# convert into correct format for padding
							if is_target:
													_lowercase       :	List[str]           =   [self._extract_mel_features(snake_case    ) for waveform in speech]
													_lowercase       :	int           =   BatchFeature({"input_values": features}    )
													_lowercase       :	str           =   self.num_mel_bins
							else:
													_lowercase       :	Optional[int]           =   BatchFeature({"input_values": speech}    )

							_lowercase       :	List[Any]           =   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      ,	)

							_lowercase       :	Optional[Any]           =   feature_size_hack

							# convert input values to correct format
							_lowercase       :	int           =   padded_inputs["input_values"]
							if not isinstance(input_values[0]      ,	np.ndarray    ):
													_lowercase       :	Any           =   [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    )
							):
													_lowercase       :	Union[str, Any]           =   [array.astype(np.floataa    ) for array in input_values]
							elif isinstance(snake_case      ,	np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Tuple           =   input_values.astype(np.floataa    )

							# convert attention_mask to correct format
							_lowercase       :	Union[str, Any]           =   padded_inputs.get("attention_mask"    )
							if attention_mask is not None:
													_lowercase       :	str           =   [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:
													_lowercase       :	Dict           =   (
													    attention_mask
													    if self._get_padding_strategies(snake_case      ,	max_length=snake_case    ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													_lowercase       :	List[Any]           =   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:
													_lowercase       :	Any           =   padded_inputs.convert_to_tensors(snake_case    )

							return padded_inputs






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

							# Don't serialize these as they are derived from the other properties.
							_lowercase       :	Optional[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




