





"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowerCamelCase_								=		logging.get_logger(__name__)






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      int							=  ['''input_features''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__=80		,SCREAMING_SNAKE_CASE__=1_60_00		,SCREAMING_SNAKE_CASE__=1_60		,SCREAMING_SNAKE_CASE__=30		,SCREAMING_SNAKE_CASE__=4_00		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=False		,**SCREAMING_SNAKE_CASE__		,)			->      Tuple:





             """simple docstring"""






             super().__init__(
                 feature_size=SCREAMING_SNAKE_CASE__		,sampling_rate=SCREAMING_SNAKE_CASE__		,padding_value=SCREAMING_SNAKE_CASE__		,return_attention_mask=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)
             __SCREAMING_SNAKE_CASE   :str						=  n_fft
             __SCREAMING_SNAKE_CASE   :int						=  hop_length
             __SCREAMING_SNAKE_CASE   :List[str]						=  chunk_length
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  chunk_length * sampling_rate
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.n_samples // hop_length
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  sampling_rate
             __SCREAMING_SNAKE_CASE   :List[str]						=  mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2		,num_mel_filters=SCREAMING_SNAKE_CASE__		,min_frequency=0.0		,max_frequency=8_0_0_0.0		,sampling_rate=SCREAMING_SNAKE_CASE__		,norm='''slaney'''		,mel_scale='''slaney'''		,)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  spectrogram(
                 SCREAMING_SNAKE_CASE__		,window_function(self.n_fft		,'''hann'''							)		,frame_length=self.n_fft		,hop_length=self.hop_length		,power=2.0		,mel_filters=self.mel_filters		,log_mel='''log10'''		,)
             __SCREAMING_SNAKE_CASE   :Any						=  log_spec[:, :-1]
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.maximum(SCREAMING_SNAKE_CASE__		,log_spec.max() - 8.0							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  (log_spec + 4.0) / 4.0
             return log_spec




       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def     _UpperCamelCase     (						SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 0.0							)			->      List[np.ndarray]:





             """simple docstring"""






             if attention_mask is not None:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  np.array(SCREAMING_SNAKE_CASE__		,np.intaa							)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  []

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

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

             return normed_input_values




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = "max_length"		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      BatchFeature:





             """simple docstring"""






             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             f'''The model corresponding to this feature extractor: {self.__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.'''							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							) and len(raw_speech.shape							) > 1
             if is_batched_numpy and len(raw_speech.shape							) > 2:
                   raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  is_batched_numpy or (
                 isinstance(SCREAMING_SNAKE_CASE__		,(list, tuple)							) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)							))
             )

             if is_batched:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  [np.asarray([speech]		,dtype=np.floataa							).T for speech in raw_speech]
             elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							):
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(SCREAMING_SNAKE_CASE__		,dtype=np.floataa							)
             elif isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  raw_speech.astype(np.floataa							)

             # always return batch
             if not is_batched:
                   __SCREAMING_SNAKE_CASE   :Dict						=  [np.asarray([raw_speech]							).T]

             __SCREAMING_SNAKE_CASE   :str						=  BatchFeature({'''input_features''': raw_speech}							)

             # convert into correct format for padding

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.pad(
                 SCREAMING_SNAKE_CASE__		,padding=SCREAMING_SNAKE_CASE__		,max_length=max_length if max_length else self.n_samples		,truncation=SCREAMING_SNAKE_CASE__		,pad_to_multiple_of=SCREAMING_SNAKE_CASE__		,return_attention_mask=return_attention_mask or do_normalize		,)

             # zero-mean and unit-variance normalization
             if do_normalize:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.zero_mean_unit_var_norm(
                       padded_inputs['''input_features''']		,attention_mask=padded_inputs['''attention_mask''']		,padding_value=self.padding_value		,)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  np.stack(padded_inputs['''input_features''']		,axis=0							)

             # make sure list is in array format
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  padded_inputs.get('''input_features'''							).transpose(2		,0		,1							)

             __SCREAMING_SNAKE_CASE   :int						=  [self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__							) for waveform in input_features[0]]

             if isinstance(input_features[0]		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.asarray(SCREAMING_SNAKE_CASE__		,dtype=np.floataa							) for feature in input_features]
             else:
                   __SCREAMING_SNAKE_CASE   :int						=  input_features

             if return_attention_mask:
                   # rescale from sample (48000) to feature (3000)
                   __SCREAMING_SNAKE_CASE   :int						=  padded_inputs['''attention_mask'''][:, :: self.hop_length]

             if return_tensors is not None:
                   __SCREAMING_SNAKE_CASE   :int						=  padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__							)

             return padded_inputs




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  copy.deepcopy(self.__dict__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  self.__class__.__name__
             if "mel_filters" in output:
                   del output["mel_filters"]
             return output






"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def      __lowerCamelCase   (							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ArgumentParser('''Accelerate CLI tool''' ,   usage='''accelerate <command> [<args>]''' ,   allow_abbrev=a_							)
      __SCREAMING_SNAKE_CASE   :str						=  parser.add_subparsers(help='''accelerate command helpers'''							)

      # Register commands
      get_config_parser(subparsers=a_							)
      env_command_parser(subparsers=a_							)
      launch_command_parser(subparsers=a_							)
      tpu_command_parser(subparsers=a_							)
      test_command_parser(subparsers=a_							)

      # Let's go
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      if not hasattr(a_ ,   '''func'''							):
            parser.print_help()
            exit(1							)

      # Run
      args.func(a_							)


if __name__ == "__main__":
  main()