

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  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[Any]          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=128 ,				snake_case=1_024 ,				snake_case=0.0 ,				snake_case=True ,				snake_case=-4.2_677_393 ,				snake_case=4.5_689_974 ,				snake_case=False ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							str    =							num_mel_bins
        lowerCAmelCase__      :							str    =							max_length
        lowerCAmelCase__      :							Dict    =							do_normalize
        lowerCAmelCase__      :							int    =							mean
        lowerCAmelCase__      :							Optional[int]    =							std
        lowerCAmelCase__      :							str    =							return_attention_mask






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							torch.from_numpy(snake_case      ).unsqueeze(0      )
        lowerCAmelCase__      :							List[Any]    =							ta_kaldi.fbank(
            snake_case ,				htk_compat=snake_case ,				sample_frequency=self.sampling_rate ,				use_energy=snake_case ,				window_type="hanning" ,				num_mel_bins=self.num_mel_bins ,				dither=0.0 ,				frame_shift=10 ,				)

        lowerCAmelCase__      :							Optional[int]    =							fbank.shape[0]
        lowerCAmelCase__      :							int    =							max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
         lowerCAmelCase__      :							List[str]    =							torch.nn.ZeroPadad((0, 0, 0, difference)      )
         lowerCAmelCase__      :							List[Any]    =							pad_module(snake_case      )
        elif difference < 0:
         lowerCAmelCase__      :							List[str]    =							fbank[0:max_length, :]

        lowerCAmelCase__      :							int    =							fbank.numpy()

        return fbank






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        return (input_values - (self.mean)) / (self.std * 2)






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





        """simple docstring"""

        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."      )

        lowerCAmelCase__      :							Any    =							isinstance(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__      :							List[Any]    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

        if is_batched:
         lowerCAmelCase__      :							Union[str, Any]    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for speech in raw_speech]
        elif not is_batched and not isinstance(snake_case ,				np.ndarray      ):
         lowerCAmelCase__      :							Optional[Any]    =							np.asarray(snake_case ,				dtype=np.floataa      )
        elif isinstance(snake_case ,				np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							raw_speech.astype(np.floataa      )

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

        # extract fbank features and pad/truncate to max_length
        lowerCAmelCase__      :							Any    =							[self._extract_fbank_features(snake_case ,				max_length=self.max_length      ) for waveform in raw_speech]

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

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

        # normalization
        if self.do_normalize:
         lowerCAmelCase__      :							int    =							[self.normalize(snake_case      ) for feature in input_values]

        if return_tensors is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs









"""simple docstring"""

def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 return 1 if input_a == 0 else 0



def 			SCREAMING_SNAKE_CASE       (   ) ->	None:
 assert not_gate(0      ) == 1
 assert not_gate(1      ) == 0


if __name__ == "__main__":
    print(not_gate(0))
    print(not_gate(1))






