
"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase      :Dict	    =		logging.get_logger(__name__)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Dict													=['''input_features''', '''attention_mask''']

     def __init__(	self:						Optional[Any]     ,		__a:						Optional[Any]=80     ,		__a:						Any=16_000     ,		__a:						List[str]=0.0     ,		__a:						Any=10     ,		__a:						List[Any]=25     ,		__a:						Union[str, Any]="hamming_window"     ,		__a:						Optional[int]=32_768.0     ,		__a:						Optional[Any]=0.97     ,		__a:						List[str]=1.0     ,		__a:						Optional[int]=True     ,		__a:						Union[str, Any]=True     ,		__a:						Tuple=False     ,		**__a:						Tuple     ,		)->    Optional[int]:
           super().__init__(feature_size=__a     ,		sampling_rate=__a     ,		padding_value=__a     ,		**__a	)

           lowerCamelCase					:					Union[str, Any]        =	feature_size
           lowerCamelCase					:					Dict        =	sampling_rate
           lowerCamelCase					:					int        =	padding_value
           lowerCamelCase					:					Tuple        =	hop_length
           lowerCamelCase					:					List[Any]        =	win_length
           lowerCamelCase					:					Dict        =	frame_signal_scale
           lowerCamelCase					:					Union[str, Any]        =	preemphasis_coeff
           lowerCamelCase					:					Tuple        =	mel_floor
           lowerCamelCase					:					Union[str, Any]        =	normalize_means
           lowerCamelCase					:					Tuple        =	normalize_vars
           lowerCamelCase					:					Dict        =	win_function
           lowerCamelCase					:					Dict        =	return_attention_mask

           lowerCamelCase					:					Any        =	win_length * sampling_rate // 1_000
           lowerCamelCase					:					Any        =	hop_length * sampling_rate // 1_000

           lowerCamelCase					:					Dict        =	optimal_fft_length(self.sample_size	)
           lowerCamelCase					:					Any        =	(self.n_fft // 2) + 1

     def 		a__						(	self:						int     ,		__a:						np.array	)->    np.ndarray:
           if self.win_function == "hamming_window":
                 lowerCamelCase					:					str        =	window_function(window_length=self.sample_size     ,		name=self.win_function     ,		periodic=__a	)
           else:
                 lowerCamelCase					:					Union[str, Any]        =	window_function(window_length=self.sample_size     ,		name=self.win_function	)

           lowerCamelCase					:					List[str]        =	mel_filter_bank(
               num_frequency_bins=self.n_freqs     ,		num_mel_filters=self.feature_size     ,		min_frequency=0.0     ,		max_frequency=self.sampling_rate / 2.0     ,		sampling_rate=self.sampling_rate     ,		)

           lowerCamelCase					:					Dict        =	spectrogram(
               one_waveform * self.frame_signal_scale     ,		window=__a     ,		frame_length=self.sample_size     ,		hop_length=self.sample_stride     ,		fft_length=self.n_fft     ,		center=__a     ,		preemphasis=self.preemphasis_coeff     ,		mel_filters=__a     ,		mel_floor=self.mel_floor     ,		log_mel="""log"""     ,		)
           return msfc_features.T

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[Any]     ,		__a:						Tuple     ,		__a:						List[Any]	)->    List[str]:
           # make sure we normalize float32 arrays
           if self.normalize_means:
                 lowerCamelCase					:					Optional[int]        =	x[:input_length].mean(axis=0	)
                 lowerCamelCase					:					Union[str, Any]        =	np.subtract(__a     ,		__a	)
           if self.normalize_vars:
                 lowerCamelCase					:					int        =	x[:input_length].std(axis=0	)
                 lowerCamelCase					:					List[str]        =	np.divide(__a     ,		__a	)

           if input_length < x.shape[0]:
                 lowerCamelCase					:					List[Any]        =	padding_value

           # make sure array is in float32
           lowerCamelCase					:					Optional[Any]        =	x.astype(np.floataa	)

           return x

     def 		a__						(	self:						Union[str, Any]     ,		__a:						List[np.ndarray]     ,		__a:						Optional[np.ndarray] = None	)->    List[np.ndarray]:
           lowerCamelCase					:					Union[str, Any]        =	attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
           return [self._normalize_one(__a     ,		__a     ,		self.padding_value	) for x, n in zip(__a     ,		__a	)]







     def __call__(	self:						int     ,		__a:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		__a:						Union[bool, str, PaddingStrategy] = False     ,		__a:						Optional[int] = None     ,		__a:						bool = False     ,		__a:						Optional[int] = None     ,		__a:						Optional[bool] = None     ,		__a:						Optional[Union[str, TensorType]] = None     ,		__a:						Optional[int] = None     ,		**__a:						Any     ,		)->    BatchFeature:

           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					:					str        =	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}'	)
           lowerCamelCase					:					int        =	is_batched_numpy or (
               isinstance(__a     ,		(list, tuple)	) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)	))
           )

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

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

           # extract fbank features
           lowerCamelCase					:					Optional[int]        =	[self._extract_mfsc_features(__a	) for one_waveform in raw_speech]

           # convert into correct format for padding
           lowerCamelCase					:					Dict        =	BatchFeature({"""input_features""": features}	)

           lowerCamelCase					:					Dict        =	self.pad(
               __a     ,		padding=__a     ,		max_length=__a     ,		truncation=__a     ,		pad_to_multiple_of=__a     ,		return_attention_mask=__a     ,		**__a     ,		)
           # make sure list is in array format
           lowerCamelCase					:					int        =	padded_inputs.get("""input_features"""	)
           if isinstance(input_features[0]     ,		__a	):
                 lowerCamelCase					:					Optional[Any]        =	[np.asarray(__a     ,		dtype=np.floataa	) for feature in input_features]

           lowerCamelCase					:					Optional[int]        =	padded_inputs.get("""attention_mask"""	)
           if attention_mask is not None:
                 lowerCamelCase					:					Optional[Any]        =	[np.asarray(__a     ,		dtype=np.intaa	) for array in attention_mask]

           if self.normalize_means or self.normalize_vars:
                 lowerCamelCase					:					Tuple        =	(
                     np.array(__a     ,		dtype=np.intaa	)
                     if self._get_padding_strategies(__a     ,		max_length=__a	) is not PaddingStrategy.DO_NOT_PAD
                     and padding
                     else None
                 )
                 lowerCamelCase					:					Union[str, Any]        =	self.normalize(
                     padded_inputs["""input_features"""]     ,		attention_mask=__a	)

           if return_tensors is not None:
                 lowerCamelCase					:					Optional[int]        =	padded_inputs.convert_to_tensors(__a	)

           return padded_inputs








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




