




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


__magic_name__  :  Tuple					=       logging.get_logger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Union[str, Any]				= ["input_features", "is_longer"]
     def __init__(						self	:		List[str]       ,						__lowerCamelCase	:		Optional[Any]=64       ,						__lowerCamelCase	:		Union[str, Any]=4_80_00       ,						__lowerCamelCase	:		str=4_80       ,						__lowerCamelCase	:		Union[str, Any]=10       ,						__lowerCamelCase	:		Tuple=10_24       ,						__lowerCamelCase	:		Any=0.0       ,						__lowerCamelCase	:		str=False       ,						__lowerCamelCase	:		float = 0       ,						__lowerCamelCase	:		float = 1_40_00       ,						__lowerCamelCase	:		int = None       ,						__lowerCamelCase	:		str = "fusion"       ,						__lowerCamelCase	:		str = "repeatpad"       ,						**__lowerCamelCase	:		List[str]       ,						):



       """simple docstring"""
       super().__init__(
           feature_size=__lowerCamelCase       ,						sampling_rate=__lowerCamelCase       ,						padding_value=__lowerCamelCase       ,						return_attention_mask=__lowerCamelCase       ,						**__lowerCamelCase       ,						)
       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=__lowerCamelCase       ,						min_frequency=__lowerCamelCase       ,						max_frequency=__lowerCamelCase       ,						sampling_rate=__lowerCamelCase       ,						norm=__lowerCamelCase       ,						mel_scale='''htk'''       ,						)
       lowerCAmelCase__										=      mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins       ,						num_mel_filters=__lowerCamelCase       ,						min_frequency=__lowerCamelCase       ,						max_frequency=__lowerCamelCase       ,						sampling_rate=__lowerCamelCase       ,						norm='''slaney'''       ,						mel_scale='''slaney'''       ,						)
     def        A__      (						self	:		Tuple						):



       """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        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		np.array       ,						__lowerCamelCase	:		Optional[np.array] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      spectrogram(
           __lowerCamelCase       ,						window_function(self.fft_window_size       ,						'''hann'''						)       ,						frame_length=self.fft_window_size       ,						hop_length=self.hop_length       ,						power=2.0       ,						mel_filters=__lowerCamelCase       ,						log_mel='''dB'''       ,						)
       return log_mel_spectrogram.T
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		str						):



       """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(
           __lowerCamelCase       ,						size=[chunk_frames, 64]       ,						mode='''bilinear'''       ,						align_corners=__lowerCamelCase						)
       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        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		np.array       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		str						):



       """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(__lowerCamelCase						) - max_length
           lowerCAmelCase__										=      np.random.randint(0       ,						overflow + 1						)
           lowerCAmelCase__										=      waveform[idx : idx + max_length]
           lowerCAmelCase__										=      self._np_extract_fbank_features(__lowerCamelCase       ,						self.mel_filters_slaney						)[None, :]
         elif truncation == "fusion":
           lowerCAmelCase__										=      self._np_extract_fbank_features(__lowerCamelCase       ,						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(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						)
             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(__lowerCamelCase						)						)
             lowerCAmelCase__										=      np.stack(np.tile(__lowerCamelCase       ,						n_repeat + 1						)						)[:max_length]
           if padding == "repeatpad":
             lowerCAmelCase__										=      int(max_length / len(__lowerCamelCase						)						)
             lowerCAmelCase__										=      np.stack(np.tile(__lowerCamelCase       ,						__lowerCamelCase						)						)
           lowerCAmelCase__										=      np.pad(__lowerCamelCase       ,						(0, max_length - waveform.shape[0])       ,						mode='''constant'''       ,						constant_values=0						)

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

       return input_mel, longer



     def __call__(						self	:		List[str]       ,						__lowerCamelCase	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,						__lowerCamelCase	:		str = None       ,						__lowerCamelCase	:		Optional[str] = None       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		Optional[Union[str, TensorType]] = None       ,						**__lowerCamelCase	:		List[Any]       ,						):



       """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(__lowerCamelCase       ,						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(__lowerCamelCase       ,						(list, tuple)						) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)						))
       )

       if is_batched:
         lowerCAmelCase__										=      [np.asarray(__lowerCamelCase       ,						dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(__lowerCamelCase       ,						np.ndarray						):
         lowerCAmelCase__										=      np.asarray(__lowerCamelCase       ,						dtype=np.floataa						)
       elif isinstance(__lowerCamelCase       ,						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(__lowerCamelCase						)]

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

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

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

       if isinstance(input_mel[0]       ,						__lowerCamelCase						):
         lowerCAmelCase__										=      [np.asarray(__lowerCamelCase       ,						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(__lowerCamelCase						)

       if return_tensors is not None:
         lowerCAmelCase__										=      input_features.convert_to_tensors(__lowerCamelCase						)

       return input_features







import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__magic_name__  :  List[Any]					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()
  lowerCAmelCase__										=      []

  def parse_line(__lowerCAmelCase				):
    for line in fp:
      if isinstance(__lowerCAmelCase   , __lowerCAmelCase				):
        lowerCAmelCase__										=      line.decode('''UTF-8'''				)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(''' '''				):
        # process a single warning and move it to `selected_warnings`.
        if len(__lowerCAmelCase				) > 0:
          lowerCAmelCase__										=      '''\n'''.join(__lowerCAmelCase				)
          # Only keep the warnings specified in `targets`
          if any(F""": {x}: """ in warning for x in targets				):
            selected_warnings.add(__lowerCAmelCase				)
          buffer.clear()
        continue
      else:
        lowerCAmelCase__										=      line.strip()
        buffer.append(__lowerCAmelCase				)

  if from_gh:
    for filename in os.listdir(__lowerCAmelCase				):
      lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
      if not os.path.isdir(__lowerCAmelCase				):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(__lowerCAmelCase				) as fp:
          parse_line(__lowerCAmelCase				)
  else:
    try:
      with zipfile.ZipFile(__lowerCAmelCase				) as z:
        for filename in z.namelist():
          if not os.path.isdir(__lowerCAmelCase				):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(__lowerCAmelCase				) as fp:
              parse_line(__lowerCAmelCase				)
    except Exception:
      logger.warning(
          F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""				)

  return selected_warnings
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()

  lowerCAmelCase__										=      [os.path.join(__lowerCAmelCase   , __lowerCAmelCase				) for p in os.listdir(__lowerCAmelCase				) if (p.endswith('''.zip'''				) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(__lowerCAmelCase   , __lowerCAmelCase				)				)

  return selected_warnings


if __name__ == "__main__":
      def 		a_							( __lowerCAmelCase				):
        return values.split(''','''				)

      __magic_name__  :  Optional[int]					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      parser.add_argument(
          """--output_dir""",
          type=str,
          required=True,
          help="""Where to store the downloaded artifacts and other result files.""",
      )
      parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
      # optional parameters
      parser.add_argument(
          """--targets""",
          default="""DeprecationWarning,UserWarning,FutureWarning""",
          type=list_str,
          help="""Comma-separated list of target warning(s) which we want to extract.""",
      )
      parser.add_argument(
          """--from_gh""",
          action="""store_true""",
          help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      __magic_name__  :  Optional[int]					=       args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            __magic_name__  :  Optional[Any]					=       get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print("""=""" * 80)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      __magic_name__  :  Optional[Any]					=       extract_warnings(args.output_dir, args.targets)
      __magic_name__  :  Any					=       sorted(selected_warnings)
      with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

