
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase       :				Union[str, Any] =   {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase       :				Union[str, Any] =   [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					lowercase       :				str =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



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


_snake_case      :							Any     	=    logging.get_logger(__name__)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					['''input_features''', '''attention_mask''']





						def __init__(			self							:List[Any]		,    lowerCAmelCase__							:List[Any]=80		,    lowerCAmelCase__							:int=16000		,    lowerCAmelCase__							:str=0.0		,    lowerCAmelCase__							:int=10		,    lowerCAmelCase__							:List[Any]=25		,    lowerCAmelCase__							:List[str]="hamming_window"		,    lowerCAmelCase__							:int=3_27_68.0		,    lowerCAmelCase__							:str=0.97		,    lowerCAmelCase__							:List[str]=1.0		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:Tuple=True		,    lowerCAmelCase__							:str=False		,    **lowerCAmelCase__							:Optional[Any]		,    )  ->Union[str, Any]:
										super().__init__(feature_size=lowerCAmelCase__		,    sampling_rate=lowerCAmelCase__		,    padding_value=lowerCAmelCase__		,    **lowerCAmelCase__     )

										lowercase         =      feature_size
										lowercase         =      sampling_rate
										lowercase         =      padding_value
										lowercase         =      hop_length
										lowercase         =      win_length
										lowercase         =      frame_signal_scale
										lowercase         =      preemphasis_coeff
										lowercase         =      mel_floor
										lowercase         =      normalize_means
										lowercase         =      normalize_vars
										lowercase         =      win_function
										lowercase         =      return_attention_mask

										lowercase         =      win_length * sampling_rate // 1000
										lowercase         =      hop_length * sampling_rate // 1000

										lowercase         =      optimal_fft_length(self.sample_size     )
										lowercase         =      (self.n_fft // 2) + 1





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

										lowercase         =      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		,    )

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





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Dict     )  ->int:
										# make sure we normalize float32 arrays
										if self.normalize_means:
														lowercase         =      x[:input_length].mean(axis=0     )
														lowercase         =      np.subtract(lowerCAmelCase__		,    lowerCAmelCase__     )
										if self.normalize_vars:
														lowercase         =      x[:input_length].std(axis=0     )
														lowercase         =      np.divide(lowerCAmelCase__		,    lowerCAmelCase__     )

										if input_length < x.shape[0]:
														lowercase         =      padding_value

										# make sure array is in float32
										lowercase         =      x.astype(np.floataa     )

										return x





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





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

										lowercase         =      isinstance(lowerCAmelCase__		,    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(lowerCAmelCase__		,    (list, tuple)     ) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)     ))
										)

										if is_batched:
														lowercase         =      [np.asarray(lowerCAmelCase__		,    dtype=np.floataa     ) for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase__		,    np.ndarray     ):
														lowercase         =      np.asarray(lowerCAmelCase__		,    dtype=np.floataa     )
										elif isinstance(lowerCAmelCase__		,    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
										lowercase         =      [self._extract_mfsc_features(lowerCAmelCase__     ) for one_waveform in raw_speech]

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

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

										lowercase         =      padded_inputs.get("attention_mask"     )
										if attention_mask is not None:
														lowercase         =      [np.asarray(lowerCAmelCase__		,    dtype=np.intaa     ) for array in attention_mask]

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

										if return_tensors is not None:
														lowercase         =      padded_inputs.convert_to_tensors(lowerCAmelCase__     )

										return padded_inputs
