
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


UpperCAmelCase       =					logging.get_logger(__name__)







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[str]									=			["""input_features""", """attention_mask"""]





	def __init__(			self						,       snake_case=80						,       snake_case=1_6000						,       snake_case=0.0						,       snake_case=10						,       snake_case=25						,       snake_case="hamming_window"						,       snake_case=32_768.0						,       snake_case=0.97						,       snake_case=1.0						,       snake_case=True						,       snake_case=True						,       snake_case=False						,       **snake_case						,       ):
		super().__init__(feature_size=snake_case						,       sampling_rate=snake_case						,       padding_value=snake_case						,       **snake_case			)

		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						,       snake_case			):
		if self.win_function == "hamming_window":
			lowercase	    =       window_function(window_length=self.sample_size						,       name=self.win_function						,       periodic=snake_case			)
		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=snake_case						,       frame_length=self.sample_size						,       hop_length=self.sample_stride						,       fft_length=self.n_fft						,       center=snake_case						,       preemphasis=self.preemphasis_coeff						,       mel_filters=snake_case						,       mel_floor=self.mel_floor						,       log_mel='log'						,       )
		return msfc_features.T





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		# make sure we normalize float32 arrays
		if self.normalize_means:
			lowercase	    =       x[:input_length].mean(axis=0			)
			lowercase	    =       np.subtract(snake_case						,       snake_case			)
		if self.normalize_vars:
			lowercase	    =       x[:input_length].std(axis=0			)
			lowercase	    =       np.divide(snake_case						,       snake_case			)

		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						,       snake_case						,       snake_case = None			):
		lowercase	    =       attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
		return [self._normalize_one(snake_case						,       snake_case						,       self.padding_value			) for x, n in zip(snake_case						,       snake_case			)]





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

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

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

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

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

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

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

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

		return padded_inputs






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



