


'''simple docstring'''






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


a_    :			Tuple      				=			logging.get_logger(__name__)




class        a   (   __snake_case				):
 _lowerCAmelCase           =		["""input_features""", """is_longer"""]
 def __init__(       self					, __magic_name__=64					, __magic_name__=4_80_00					, __magic_name__=4_80					, __magic_name__=10					, __magic_name__=10_24					, __magic_name__=0.0					, __magic_name__=False					, __magic_name__ = 0					, __magic_name__ = 1_40_00					, __magic_name__ = None					, __magic_name__ = "fusion"					, __magic_name__ = "repeatpad"					, **__magic_name__					, )    ->							Union[str, Any]:
      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					, )
      _a						=							top_db
      _a						=							truncation
      _a						=							padding
      _a						=							fft_window_size
      _a						=							(fft_window_size >> 1) + 1
      _a						=							hop_length
      _a						=							max_length_s
      _a						=							max_length_s * sampling_rate
      _a						=							sampling_rate
      _a						=							frequency_min
      _a						=							frequency_max
      _a						=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					, num_mel_filters=__SCREAMING_SNAKE_CASE					, min_frequency=__SCREAMING_SNAKE_CASE					, max_frequency=__SCREAMING_SNAKE_CASE					, sampling_rate=__SCREAMING_SNAKE_CASE					, norm=__SCREAMING_SNAKE_CASE					, mel_scale='htk'					, )
      _a						=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					, num_mel_filters=__SCREAMING_SNAKE_CASE					, min_frequency=__SCREAMING_SNAKE_CASE					, max_frequency=__SCREAMING_SNAKE_CASE					, sampling_rate=__SCREAMING_SNAKE_CASE					, norm='slaney'					, mel_scale='slaney'					, )
 def 							__UpperCAmelCase						(       self  )    ->							Dict[str, Any]:
      _a						=							copy.deepcopy(self.__dict__  )
      _a						=							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					, __magic_name__					, __magic_name__ = None  )    ->							np.ndarray:
      _a						=							spectrogram(
          __SCREAMING_SNAKE_CASE					, window_function(self.fft_window_size					, 'hann'  )					, frame_length=self.fft_window_size					, hop_length=self.hop_length					, power=2.0					, mel_filters=__SCREAMING_SNAKE_CASE					, log_mel='dB'					, )
      return log_mel_spectrogram.T
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__					, __magic_name__  )    ->							Union[str, Any]:
      _a						=							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
           _a						=							[0]
      if len(ranges[2]  ) == 0:
           # if the audio is too short, we just use the first chunk
           _a						=							[0]
      # randomly choose index for each part
      _a						=							np.random.choice(ranges[0]  )
      _a						=							np.random.choice(ranges[1]  )
      _a						=							np.random.choice(ranges[2]  )

      _a						=							mel[idx_front : idx_front + chunk_frames, :]
      _a						=							mel[idx_middle : idx_middle + chunk_frames, :]
      _a						=							mel[idx_back : idx_back + chunk_frames, :]

      _a						=							torch.tensor(mel[None, None, :]  )
      _a						=							torch.nn.functional.interpolate(
          __SCREAMING_SNAKE_CASE					, size=[chunk_frames, 64]					, mode='bilinear'					, align_corners=__SCREAMING_SNAKE_CASE  )
      _a						=							mel_shrink[0][0].numpy()
      _a						=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					, axis=0  )
      return mel_fusion
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__					, __magic_name__					, __magic_name__  )    ->							np.array:
      if waveform.shape[0] > max_length:
           if truncation == "rand_trunc":
                _a						=							True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                _a						=							len(__SCREAMING_SNAKE_CASE  ) - max_length
                _a						=							np.random.randint(0					, overflow + 1  )
                _a						=							waveform[idx : idx + max_length]
                _a						=							self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE					, self.mel_filters_slaney  )[None, :]
           elif truncation == "fusion":
                _a						=							self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE					, self.mel_filters  )
                _a						=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                _a						=							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.
                     _a						=							np.stack([mel, mel, mel, mel]					, axis=0  )
                     _a						=							False
                else:
                     _a						=							self._random_mel_fusion(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE  )
                     _a						=							True
           else:
                raise NotImplementedError(f'data_truncating {truncation} not implemented'  )

      else:
           _a						=							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":
                     _a						=							int(max_length / len(__SCREAMING_SNAKE_CASE  )  )
                     _a						=							np.stack(np.tile(__SCREAMING_SNAKE_CASE					, n_repeat + 1  )  )[:max_length]
                if padding == "repeatpad":
                     _a						=							int(max_length / len(__SCREAMING_SNAKE_CASE  )  )
                     _a						=							np.stack(np.tile(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE  )  )
                _a						=							np.pad(__SCREAMING_SNAKE_CASE					, (0, max_length - waveform.shape[0])					, mode='constant'					, constant_values=0  )

           if truncation == "fusion":
                _a						=							self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE					, self.mel_filters  )
                _a						=							np.stack([input_mel, input_mel, input_mel, input_mel]					, axis=0  )
           else:
                _a						=							self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE					, self.mel_filters_slaney  )[None, :]

      return input_mel, longer




 def __call__(       self					, __magic_name__					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = None					, **__magic_name__					, )    ->							BatchFeature:
      _a						=							truncation if truncation is not None else self.truncation
      _a						=							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.'  )

      _a						=							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}'  )
      _a						=							is_batched_numpy or (
          isinstance(__SCREAMING_SNAKE_CASE					, (list, tuple)  ) and (isinstance(raw_speech[0]					, (np.ndarray, tuple, list)  ))
      )

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

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

      # convert to mel spectrogram, truncate and pad if needed.
      _a						=							[
          self._get_input_mel(__SCREAMING_SNAKE_CASE					, max_length if max_length else self.nb_max_samples					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE  )
          for waveform in raw_speech
      ]

      _a						=							[]
      _a						=							[]
      for mel, longer in padded_inputs:
           input_mel.append(__SCREAMING_SNAKE_CASE  )
           is_longer.append(__SCREAMING_SNAKE_CASE  )

      if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE  ) == 0:
           # if no audio is longer than 10s, then randomly select one audio to be longer
           _a						=							np.random.randint(0					, len(__SCREAMING_SNAKE_CASE  )  )
           _a						=							True

      if isinstance(input_mel[0]					, __SCREAMING_SNAKE_CASE  ):
           _a						=							[np.asarray(__SCREAMING_SNAKE_CASE					, dtype=np.floataa  ) for feature in input_mel]

      # is_longer is a list of bool
      _a						=							[[longer] for longer in is_longer]

      _a						=							{'input_features': input_mel, 'is_longer': is_longer}
      _a						=							BatchFeature(__SCREAMING_SNAKE_CASE  )

      if return_tensors is not None:
           _a						=							input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE  )

      return input_features









"""simple docstring"""





import argparse
import json
import subprocess







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		str )      ->				List[Any]:
  lowerCamelCase_         =						[]

  lowerCamelCase_         =						(
      F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowerCamelCase_         =						subprocess.run(_lowerCamelCase		,				shell=_lowerCamelCase		,				stdout=subprocess.PIPE )
  lowerCamelCase_         =						output.stdout.decode('utf-8' )
  lowerCamelCase_         =						json.loads(_lowerCamelCase )

  lowerCamelCase_         =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase )

    # save the result so we can report them on Slack
  with open('offline_runners.txt'		,				'w' ) as fp:
    fp.write(json.dumps(_lowerCamelCase ) )

  if len(_lowerCamelCase ) > 0:
    lowerCamelCase_         =						'\n'.join([x['name'] for x in offline_runners] )
    raise ValueError(F'''The following runners are offline:\n{failed}''' )


if __name__ == "__main__":







  def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				Tuple:
    return values.split(',' )

  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--target_runners''',
      default=None,
      type=list_str,
      required=True,
      help='''Comma-separated list of runners to check status.''',
  )

  parser.add_argument(
      '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
  )
  _SCREAMING_SNAKE_CASE					:				Any					    =						parser.parse_args()






  get_runner_status(args.target_runners, args.token)




