

"""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


_a					 =				logging.get_logger(__name__)




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      ["input_values", "attention_mask"]


 def __init__(     self				:     Any,   UpperCAmelCase__				:     int=1,   UpperCAmelCase__				:     int=1_6_0_0_0,   UpperCAmelCase__				:     List[str]=1_2_8,   UpperCAmelCase__				:     Any=1_0_2_4,   UpperCAmelCase__				:     Dict=0.0,   UpperCAmelCase__				:     List[str]=True,   UpperCAmelCase__				:     Optional[int]=-4.2_677_393,   UpperCAmelCase__				:     str=4.5_689_974,   UpperCAmelCase__				:     Union[str, Any]=False,   **UpperCAmelCase__				:     int,   ):
    super().__init__(feature_size=UpperCAmelCase__,   sampling_rate=UpperCAmelCase__,   padding_value=UpperCAmelCase__,   **UpperCAmelCase__      )
    __lowercase								=		num_mel_bins
    __lowercase								=		max_length
    __lowercase								=		do_normalize
    __lowercase								=		mean
    __lowercase								=		std
    __lowercase								=		return_attention_mask


 def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     np.ndarray,   UpperCAmelCase__				:     int,   ):
    __lowercase								=		torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
    __lowercase								=		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=1_0,   )

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

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

    __lowercase								=		fbank.numpy()

    return fbank


 def    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     np.ndarray      ):
    return (input_values - (self.mean)) / (self.std * 2)



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

    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."      )

    __lowercase								=		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}"""      )
    __lowercase								=		is_batched_numpy or (
        isinstance(UpperCAmelCase__,   (list, tuple)      ) and (isinstance(raw_speech[0],   (np.ndarray, tuple, list)      ))
    )

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

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

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

    # convert into BatchFeature
    __lowercase								=		BatchFeature({"input_values": features}      )

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

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

    if return_tensors is not None:
       __lowercase								=		padded_inputs.convert_to_tensors(UpperCAmelCase__      )

    return padded_inputs







"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int.")
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

   __lowercase								=		(math.floor(math.log(n * (phi + 2),     UpperCamelCase_) - 1) // 3) * 3 + 2
   __lowercase								=		Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
   __lowercase								=		num // 2
   return int(UpperCamelCase_)


if __name__ == "__main__":
   print(F"{solution() = }")




