'''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


SCREAMING_SNAKE_CASE_						 =      logging.get_logger(__name__)




class  a			(							snake_case_					):



  """simple docstring"""
  __lowerCAmelCase					   =       ["""audio_values""", """audio_mask"""]







  def __init__(				self ,     snake_case_=2048 ,     snake_case_=1 ,     snake_case_=[16, 16] ,     snake_case_=128 ,     snake_case_=4_4100 ,     snake_case_=86 ,     snake_case_=2048 ,     snake_case_=0.0 ,     **snake_case_ ,     ):

    '''simple docstring'''







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

    __UpperCAmelCase:       Tuple		=			spectrogram_length
    __UpperCAmelCase:       List[Any]		=			num_channels
    __UpperCAmelCase:       Any		=			patch_size
    __UpperCAmelCase:       Union[str, Any]		=			feature_size // self.patch_size[1]
    __UpperCAmelCase:       Tuple		=			n_fft
    __UpperCAmelCase:       Optional[Any]		=			sampling_rate // hop_length_to_sampling_rate
    __UpperCAmelCase:       Tuple		=			sampling_rate
    __UpperCAmelCase:       str		=			padding_value
    __UpperCAmelCase:       List[str]		=			mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2 ,     num_mel_filters=A_ ,     min_frequency=0.0 ,     max_frequency=2_2050.0 ,     sampling_rate=A_ ,     norm="""slaney""" ,     mel_scale="""slaney""" ,     ).T







  def  lowercase_	(				self ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       List[str]		=			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 ,     )
    __UpperCAmelCase:       Optional[Any]		=			log_spec[:, :-1]
    __UpperCAmelCase:       Optional[int]		=			log_spec - 2_0.0
    __UpperCAmelCase:       Any		=			np.clip(log_spec / 4_0.0 ,     -2.0 ,     0.0       ) + 1.0
    return log_spec







  def __call__(				self ,     snake_case_ ,     snake_case_ = None ,     snake_case_ = True ,     snake_case_ = None ,     snake_case_ = False ,     snake_case_ = False ,     **snake_case_ ,     ):

    '''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."""       )

    __UpperCAmelCase:       Tuple		=			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}'''       )
    __UpperCAmelCase:       Tuple		=			is_batched_numpy or (
        isinstance(A_ ,     (list, tuple)       ) and (isinstance(raw_speech[0] ,     (np.ndarray, tuple, list)       ))
    )
    if is_batched:
      __UpperCAmelCase:       Union[str, Any]		=			[np.asarray([speech] ,     dtype=np.floataa       ).T for speech in raw_speech]
    elif not is_batched and not isinstance(A_ ,     np.ndarray       ):
      __UpperCAmelCase:       List[str]		=			np.asarray(A_ ,     dtype=np.floataa       )
    elif isinstance(A_ ,     np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
      __UpperCAmelCase:       Any		=			raw_speech.astype(np.floataa       )
    # always return batch
    if not is_batched:
      __UpperCAmelCase:       str		=			[np.asarray([raw_speech]       ).T]

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

    # Create audio attention mask
    __UpperCAmelCase:       List[str]		=			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:
      __UpperCAmelCase:       Union[str, Any]		=			[
          (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
      ]
      __UpperCAmelCase:       int		=			np.array(A_       ).astype(np.floataa       )

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

    # return as BatchFeature
    if return_attention_mask:
      __UpperCAmelCase:       Any		=			{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
    else:
      __UpperCAmelCase:       List[str]		=			{"""audio_values""": padded_audio_features}

    __UpperCAmelCase:       List[str]		=			BatchFeature(data=A_ ,     tensor_type=A_       )
    return encoded_inputs

'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




