






"""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


_SCREAMING_SNAKE_CASE					:				Tuple					    =						logging.get_logger(__name__)






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Optional[int]						= ["""input_features""", """is_longer"""]






    def __init__(       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							int=64       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=48000       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=480       ,						__SCREAMING_SNAKE_CASE     :							Tuple=10       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=1024       ,						__SCREAMING_SNAKE_CASE     :							Tuple=0.0       ,						__SCREAMING_SNAKE_CASE     :							Any=False       ,						__SCREAMING_SNAKE_CASE     :							float = 0       ,						__SCREAMING_SNAKE_CASE     :							float = 14000       ,						__SCREAMING_SNAKE_CASE     :							int = None       ,						__SCREAMING_SNAKE_CASE     :							str = "fusion"       ,						__SCREAMING_SNAKE_CASE     :							str = "repeatpad"       ,						**__SCREAMING_SNAKE_CASE     :							Dict       ,						)     ->		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       ,						)
      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=__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'       ,						)
      lowerCamelCase_         =						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     :							Optional[int]					)     ->		Dict[str, Any]:
      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    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							np.array       ,						__SCREAMING_SNAKE_CASE     :							Optional[np.array] = None					)     ->		np.ndarray:
      lowerCamelCase_         =						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     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]					)     ->		Union[str, Any]:
      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(
          __SCREAMING_SNAKE_CASE       ,						size=[chunk_frames, 64]       ,						mode='bilinear'       ,						align_corners=__SCREAMING_SNAKE_CASE					)
      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    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							np.array       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		np.array:
      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(__SCREAMING_SNAKE_CASE					) - max_length
          lowerCamelCase_         =						np.random.randint(0       ,						overflow + 1					)
          lowerCamelCase_         =						waveform[idx : idx + max_length]
          lowerCamelCase_         =						self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE       ,						self.mel_filters_slaney					)[None, :]
        elif truncation == "fusion":
          lowerCamelCase_         =						self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE       ,						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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
            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(__SCREAMING_SNAKE_CASE					)					)
            lowerCamelCase_         =						np.stack(np.tile(__SCREAMING_SNAKE_CASE       ,						n_repeat + 1					)					)[:max_length]
          if padding == "repeatpad":
            lowerCamelCase_         =						int(max_length / len(__SCREAMING_SNAKE_CASE					)					)
            lowerCamelCase_         =						np.stack(np.tile(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)					)
          lowerCamelCase_         =						np.pad(__SCREAMING_SNAKE_CASE       ,						(0, max_length - waveform.shape[0])       ,						mode='constant'       ,						constant_values=0					)

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

      return input_mel, longer






    def __call__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,						__SCREAMING_SNAKE_CASE     :							str = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, TensorType]] = None       ,						**__SCREAMING_SNAKE_CASE     :							List[str]       ,						)     ->		BatchFeature:
      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(__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}'''					)
      lowerCamelCase_         =						is_batched_numpy or (
          isinstance(__SCREAMING_SNAKE_CASE       ,						(list, tuple)					) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)					))
      )

      if is_batched:
        lowerCamelCase_         =						[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					):
        lowerCamelCase_         =						np.asarray(__SCREAMING_SNAKE_CASE       ,						dtype=np.floataa					)
      elif isinstance(__SCREAMING_SNAKE_CASE       ,						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(__SCREAMING_SNAKE_CASE					)]

      # convert to mel spectrogram, truncate and pad if needed.
      lowerCamelCase_         =						[
          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
      ]

      lowerCamelCase_         =						[]
      lowerCamelCase_         =						[]
      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
        lowerCamelCase_         =						np.random.randint(0       ,						len(__SCREAMING_SNAKE_CASE					)					)
        lowerCamelCase_         =						True

      if isinstance(input_mel[0]       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						[np.asarray(__SCREAMING_SNAKE_CASE       ,						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(__SCREAMING_SNAKE_CASE					)

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

      return input_features












"""simple docstring"""





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_SCREAMING_SNAKE_CASE					:				Any					    =						importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
  from .safilesystem import SaFileSystem  # noqa: F401

_SCREAMING_SNAKE_CASE					:				List[compression.BaseCompressedFileFileSystem]					    =						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
  if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
    warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
  fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				str:
  if "://" in dataset_path:
    lowerCamelCase_         =						dataset_path.split('://' )[1]
  return dataset_path







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem )      ->				bool:
  if fs is not None and fs.protocol != "file":
    return True
  else:
    return False







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		str )      ->				int:
  lowerCamelCase_         =						not is_remote_filesystem(_lowerCamelCase )
  if is_local:
    # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
    shutil.move(fs._strip_protocol(_lowerCamelCase )		,				fs._strip_protocol(_lowerCamelCase ) )
  else:
    fs.mv(_lowerCamelCase		,				_lowerCamelCase		,				recursive=_lowerCamelCase )







def      lowerCamelCase__       (							)      ->				None:
  if hasattr(fsspec.asyn		,				'reset_lock' ):
    # for future fsspec>2022.05.0
    fsspec.asyn.reset_lock()
  else:
    lowerCamelCase_         =						None
    lowerCamelCase_         =						None
    lowerCamelCase_         =						threading.Lock()




