





'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase_											=  logging.get_logger(__name__)




class  __UpperCamelCase						(       A__						):
     __A  :     List[str]            =      ["""input_values""", """attention_mask"""]





     def __init__(    self							,       _UpperCamelCase=1							,       _UpperCamelCase=16000							,       _UpperCamelCase=128							,       _UpperCamelCase=1024							,       _UpperCamelCase=0.0							,       _UpperCamelCase=True							,       _UpperCamelCase=-4.2677393							,       _UpperCamelCase=4.5689974							,       _UpperCamelCase=False							,       **_UpperCamelCase							,       ):
        super().__init__(feature_size=_UpperCamelCase							,       sampling_rate=_UpperCamelCase							,       padding_value=_UpperCamelCase							,       **_UpperCamelCase				)
        _UpperCAmelCase											=      num_mel_bins
        _UpperCAmelCase											=      max_length
        _UpperCAmelCase											=      do_normalize
        _UpperCAmelCase											=      mean
        _UpperCAmelCase											=      std
        _UpperCAmelCase											=      return_attention_mask





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       ):
        _UpperCAmelCase											=      torch.from_numpy(_UpperCamelCase				).unsqueeze(0				)
        _UpperCAmelCase											=      ta_kaldi.fbank(
            _UpperCamelCase							,       htk_compat=_UpperCamelCase							,       sample_frequency=self.sampling_rate							,       use_energy=_UpperCamelCase							,       window_type='''hanning'''							,       num_mel_bins=self.num_mel_bins							,       dither=0.0							,       frame_shift=10							,       )

        _UpperCAmelCase											=      fbank.shape[0]
        _UpperCAmelCase											=      max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
           _UpperCAmelCase											=      torch.nn.ZeroPadad((0, 0, 0, difference)				)
           _UpperCAmelCase											=      pad_module(_UpperCamelCase				)
        elif difference < 0:
           _UpperCAmelCase											=      fbank[0:max_length, :]

        _UpperCAmelCase											=      fbank.numpy()

        return fbank





     def 		UpperCamelCase(    self							,       _UpperCamelCase				):
        return (input_values - (self.mean)) / (self.std * 2)





     def __call__(    self							,       _UpperCamelCase							,       _UpperCamelCase = None							,       _UpperCamelCase = None							,       **_UpperCamelCase							,       ):

        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 `raw_speech` 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.'''				)

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

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

        # always return batch
        if not is_batched:
           _UpperCAmelCase											=      [raw_speech]

        # extract fbank features and pad/truncate to max_length
        _UpperCAmelCase											=      [self._extract_fbank_features(_UpperCamelCase							,       max_length=self.max_length				) for waveform in raw_speech]

        # convert into BatchFeature
        _UpperCAmelCase											=      BatchFeature({'''input_values''': features}				)

        # make sure list is in array format
        _UpperCAmelCase											=      padded_inputs.get('''input_values'''				)
        if isinstance(input_values[0]							,       _UpperCamelCase				):
           _UpperCAmelCase											=      [np.asarray(_UpperCamelCase							,       dtype=np.floataa				) for feature in input_values]

        # normalization
        if self.do_normalize:
           _UpperCAmelCase											=      [self.normalize(_UpperCamelCase				) for feature in input_values]

        if return_tensors is not None:
           _UpperCAmelCase											=      padded_inputs.convert_to_tensors(_UpperCamelCase				)

        return padded_inputs






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_											=  {
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_											=  ["OneFormerImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_											=  [
       "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "OneFormerForUniversalSegmentation",
       "OneFormerModel",
       "OneFormerPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
   from .processing_oneformer import OneFormerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_oneformer import OneFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_oneformer import (
          ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          OneFormerForUniversalSegmentation,
          OneFormerModel,
          OneFormerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_											=  _LazyModule(__name__, globals()["__file__"], _import_structure)