





'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowercase_      							=					logging.get_logger(__name__)


class    a_	(							snake_case_			):

    '''simple docstring'''
    UpperCamelCase					       =					["""input_features"""]


    def __init__(						self					,						A=80					,						A=1_6000					,						A=160					,						A=30					,						A=400					,						A=0.0					,						A=False					,						**A					,						)						->  Any:
        super().__init__(
            feature_size=UpperCamelCase_					,						sampling_rate=UpperCamelCase_					,						padding_value=UpperCamelCase_					,						return_attention_mask=UpperCamelCase_					,						**UpperCamelCase_					,						)
        _SCREAMING_SNAKE_CASE   							=     n_fft
        _SCREAMING_SNAKE_CASE   							=     hop_length
        _SCREAMING_SNAKE_CASE   							=     chunk_length
        _SCREAMING_SNAKE_CASE   							=     chunk_length * sampling_rate
        _SCREAMING_SNAKE_CASE   							=     self.n_samples // hop_length
        _SCREAMING_SNAKE_CASE   							=     sampling_rate
        _SCREAMING_SNAKE_CASE   							=     mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2					,						num_mel_filters=UpperCamelCase_					,						min_frequency=0.0					,						max_frequency=8000.0					,						sampling_rate=UpperCamelCase_					,						norm="""slaney"""					,						mel_scale="""slaney"""					,						)


    def      snake_case_(						self					,						A			)						->  Union[str, Any]:
        _SCREAMING_SNAKE_CASE   							=     spectrogram(
            UpperCamelCase_					,						window_function(self.n_fft					,						"""hann"""			)					,						frame_length=self.n_fft					,						hop_length=self.hop_length					,						power=2.0					,						mel_filters=self.mel_filters					,						log_mel="""log10"""					,						)
        _SCREAMING_SNAKE_CASE   							=     log_spec[:, :-1]
        _SCREAMING_SNAKE_CASE   							=     np.maximum(UpperCamelCase_					,						log_spec.max() - 8.0			)
        _SCREAMING_SNAKE_CASE   							=     (log_spec + 4.0) / 4.0
        return log_spec


    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def      snake_case_(						A					,						A					,						A = 0.0			)						->  Tuple:
        if attention_mask is not None:
            _SCREAMING_SNAKE_CASE   							=     np.array(UpperCamelCase_					,						np.intaa			)
            _SCREAMING_SNAKE_CASE   							=     []

            for vector, length in zip(UpperCamelCase_					,						attention_mask.sum(-1			)			):
                _SCREAMING_SNAKE_CASE   							=     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
                if length < normed_slice.shape[0]:
                    _SCREAMING_SNAKE_CASE   							=     padding_value

                normed_input_values.append(UpperCamelCase_			)
        else:
            _SCREAMING_SNAKE_CASE   							=     [(x - x.mean()) / np.sqrt(x.var() + 1e-7			) for x in input_values]

        return normed_input_values


    def __call__(						self					,						A					,						A = True					,						A = None					,						A = None					,						A = None					,						A = "max_length"					,						A = None					,						A = None					,						A = None					,						**A					,						)						->  List[str]:

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                    f' was sampled 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."""			)

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

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

        # always return batch
        if not is_batched:
            _SCREAMING_SNAKE_CASE   							=     [np.asarray([raw_speech]			).T]

        _SCREAMING_SNAKE_CASE   							=     BatchFeature({"""input_features""": raw_speech}			)

        # convert into correct format for padding

        _SCREAMING_SNAKE_CASE   							=     self.pad(
            UpperCamelCase_					,						padding=UpperCamelCase_					,						max_length=max_length if max_length else self.n_samples					,						truncation=UpperCamelCase_					,						pad_to_multiple_of=UpperCamelCase_					,						return_attention_mask=return_attention_mask or do_normalize					,						)

        # zero-mean and unit-variance normalization
        if do_normalize:
            _SCREAMING_SNAKE_CASE   							=     self.zero_mean_unit_var_norm(
                padded_inputs["""input_features"""]					,						attention_mask=padded_inputs["""attention_mask"""]					,						padding_value=self.padding_value					,						)
            _SCREAMING_SNAKE_CASE   							=     np.stack(padded_inputs["""input_features"""]					,						axis=0			)

        # make sure list is in array format
        _SCREAMING_SNAKE_CASE   							=     padded_inputs.get("""input_features"""			).transpose(2					,						0					,						1			)

        _SCREAMING_SNAKE_CASE   							=     [self._np_extract_fbank_features(UpperCamelCase_			) for waveform in input_features[0]]

        if isinstance(input_features[0]					,						UpperCamelCase_			):
            _SCREAMING_SNAKE_CASE   							=     [np.asarray(UpperCamelCase_					,						dtype=np.floataa			) for feature in input_features]
        else:
            _SCREAMING_SNAKE_CASE   							=     input_features

        if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            _SCREAMING_SNAKE_CASE   							=     padded_inputs['attention_mask'][:, :: self.hop_length]

        if return_tensors is not None:
            _SCREAMING_SNAKE_CASE   							=     padded_inputs.convert_to_tensors(UpperCamelCase_			)

        return padded_inputs





    def      snake_case_(						self			)						->  Any:
        _SCREAMING_SNAKE_CASE   							=     copy.deepcopy(self.__dict__			)
        _SCREAMING_SNAKE_CASE   							=     self.__class__.__name__
        if "mel_filters" in output:
            del output["mel_filters"]
        return output





"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return int((input_a, input_a).count(0 ) == 0 )
def 					lowerCamelCase   (     ):
    assert and_gate(0    ,0 ) == 0
    assert and_gate(0    ,1 ) == 0
    assert and_gate(1    ,0 ) == 0
    assert and_gate(1    ,1 ) == 1


if __name__ == "__main__":
    test_and_gate()
    print(and_gate(1, 0))
    print(and_gate(0, 0))
    print(and_gate(0, 1))
    print(and_gate(1, 1))

