



"""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


_lowercase : int      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['input_features', 'attention_mask']



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Union[str, Any]=80,  lowerCamelCase							:  Optional[int]=1_6000,  lowerCamelCase							:  Any=0.0,  lowerCamelCase							:  Dict=10,  lowerCamelCase							:  Optional[int]=25,  lowerCamelCase							:  int="hamming_window",  lowerCamelCase							:  int=32_768.0,  lowerCamelCase							:  str=0.97,  lowerCamelCase							:  Optional[int]=1.0,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Any=False,  **lowerCamelCase							:  Union[str, Any],  )->					Optional[int]:
								super().__init__(feature_size=lowerCamelCase,  sampling_rate=lowerCamelCase,  padding_value=lowerCamelCase,  **lowerCamelCase     )

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

								lowerCamelCase__		:		str			=win_length * sampling_rate // 1000
								lowerCamelCase__		:		Dict			=hop_length * sampling_rate // 1000

								lowerCamelCase__		:		Dict			=optimal_fft_length(self.sample_size     )
								lowerCamelCase__		:		List[Any]			=(self.n_fft // 2) + 1



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

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

								if input_length < x.shape[0]:
											lowerCamelCase__		:		Optional[Any]			=padding_value

								# make sure array is in float32
								lowerCamelCase__		:		Union[str, Any]			=x.astype(np.floataa     )

								return x



					def 		snake_case       (       self							:  Dict,  lowerCamelCase							:  List[np.ndarray],  lowerCamelCase							:  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(lowerCamelCase,  lowerCamelCase,  self.padding_value     ) for x, n in zip(lowerCamelCase,  lowerCamelCase     )]




					def __call__(       self							:  int,  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							:  Dict,  )->					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__		:		Any			=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}'''     )
								lowerCamelCase__		:		Optional[int]			=is_batched_numpy or (
								    isinstance(lowerCamelCase,  (list, tuple)     ) and (isinstance(raw_speech[0],  (np.ndarray, tuple, list)     ))
								)

								if is_batched:
											lowerCamelCase__		:		Union[str, Any]			=[np.asarray(lowerCamelCase,  dtype=np.floataa     ) for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCamelCase,  np.ndarray     ):
											lowerCamelCase__		:		str			=np.asarray(lowerCamelCase,  dtype=np.floataa     )
								elif isinstance(lowerCamelCase,  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__		:		Dict			=[raw_speech]

								# extract fbank features
								lowerCamelCase__		:		Dict			=[self._extract_mfsc_features(lowerCamelCase     ) for one_waveform in raw_speech]

								# convert into correct format for padding
								lowerCamelCase__		:		List[str]			=BatchFeature({'''input_features''': features}     )

								lowerCamelCase__		:		Dict			=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
								lowerCamelCase__		:		Dict			=padded_inputs.get('''input_features'''     )
								if isinstance(input_features[0],  lowerCamelCase     ):
											lowerCamelCase__		:		Tuple			=[np.asarray(lowerCamelCase,  dtype=np.floataa     ) for feature in input_features]

								lowerCamelCase__		:		Tuple			=padded_inputs.get('''attention_mask'''     )
								if attention_mask is not None:
											lowerCamelCase__		:		str			=[np.asarray(lowerCamelCase,  dtype=np.intaa     ) for array in attention_mask]

								if self.normalize_means or self.normalize_vars:
											lowerCamelCase__		:		str			=(
											    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
											)
											lowerCamelCase__		:		Tuple			=self.normalize(
											    padded_inputs['''input_features'''],  attention_mask=lowerCamelCase     )

								if return_tensors is not None:
											lowerCamelCase__		:		Dict			=padded_inputs.convert_to_tensors(lowerCamelCase     )

								return padded_inputs









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

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




