


"""simple docstring"""


from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


a_       							=    logging.get_logger(__name__)
class snake_case      (  __snake_case):
  __UpperCamelCase           =			['audio_values', 'audio_mask']







  def __init__(					self      :       Optional[int]    ,   a__      :       List[str]=20_48    ,   a__      :       str=1    ,   a__      :       Union[str, Any]=[16, 16]    ,   a__      :       Optional[int]=1_28    ,   a__      :       Any=4_41_00    ,   a__      :       Tuple=86    ,   a__      :       Optional[Any]=20_48    ,   a__      :       str=0.0    ,   **a__      :       int    ,   )       ->						List[str]:





      '''simple docstring'''







      super().__init__(
          feature_size=A_    ,   sampling_rate=A_    ,   padding_value=A_    ,   **A_    ,   )

      _A					  =			spectrogram_length
      _A					  =			num_channels
      _A					  =			patch_size
      _A					  =			feature_size // self.patch_size[1]
      _A					  =			n_fft
      _A					  =			sampling_rate // hop_length_to_sampling_rate
      _A					  =			sampling_rate
      _A					  =			padding_value
      _A					  =			mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2    ,   num_mel_filters=A_    ,   min_frequency=0.0    ,   max_frequency=2_20_50.0    ,   sampling_rate=A_    ,   norm="slaney"    ,   mel_scale="slaney"    ,   ).T







  def  a_ (					self      :       Optional[int]    ,   a__      :       Tuple		)       ->						Any:





      '''simple docstring'''







      _A					  =			spectrogram(
          A_    ,   window_function(self.n_fft    ,   "hann"		)    ,   frame_length=self.n_fft    ,   hop_length=self.hop_length    ,   power=2.0    ,   mel_filters=self.mel_filters.T    ,   log_mel="dB"    ,   db_range=8_0.0    ,   )
      _A					  =			log_spec[:, :-1]
      _A					  =			log_spec - 2_0.0
      _A					  =			np.clip(log_spec / 4_0.0    ,   -2.0    ,   0.0		) + 1.0
      return log_spec







  def __call__(					self      :       Union[str, Any]    ,   a__      :       str    ,   a__      :       Any = None    ,   a__      :       List[Any] = True    ,   a__      :       Union[str, Any] = None    ,   a__      :       str = False    ,   a__      :       Tuple = False    ,   **a__      :       List[str]    ,   )       ->						List[str]:





      '''simple docstring'''







      if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
              raise ValueError(
                  "This feature extractor is set to support sampling rate"
                  F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                  F""" 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(A_    ,   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(A_    ,   (list, tuple)		) and (isinstance(raw_speech[0]    ,   (np.ndarray, tuple, list)		))
      )
      if is_batched:
          _A					  =			[np.asarray([speech]    ,   dtype=np.floataa		).T for speech in raw_speech]
      elif not is_batched and not isinstance(A_    ,   np.ndarray		):
          _A					  =			np.asarray(A_    ,   dtype=np.floataa		)
      elif isinstance(A_    ,   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([raw_speech]		).T]

      # Convert audio signals to log mel spectrograms, truncate by time axis
      _A					  =			[
          self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
      ]
      if isinstance(audio_features[0]    ,   A_		):
          _A					  =			[np.asarray(A_    ,   dtype=np.floataa		) for feature in audio_features]

      # Create audio attention mask
      _A					  =			max(
          [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
      if return_attention_mask:
          _A					  =			[
              (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
              + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
              for feature in audio_features
          ]
          _A					  =			np.array(A_		).astype(np.floataa		)

      # convert into correct format for padding
      _A					  =			max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
      _A					  =			np.ones([len(A_		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
      _A					  =			padded_audio_features * self.padding_value
      for i in range(len(A_		)		):
          _A					  =			audio_features[i]
          _A					  =			feature

      # return as BatchFeature
      if return_attention_mask:
          _A					  =			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
      else:
          _A					  =			{"audio_values": padded_audio_features}

      _A					  =			BatchFeature(data=A_    ,   tensor_type=A_		)
      return encoded_inputs



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)