"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MegatronBertForCausalLM',
						    'MegatronBertForMaskedLM',
						    'MegatronBertForMultipleChoice',
						    'MegatronBertForNextSentencePrediction',
						    'MegatronBertForPreTraining',
						    'MegatronBertForQuestionAnswering',
						    'MegatronBertForSequenceClassification',
						    'MegatronBertForTokenClassification',
						    'MegatronBertModel',
						    'MegatronBertPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_megatron_bert import (
												    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MegatronBertForCausalLM,
												    MegatronBertForMaskedLM,
												    MegatronBertForMultipleChoice,
												    MegatronBertForNextSentencePrediction,
												    MegatronBertForPreTraining,
												    MegatronBertForQuestionAnswering,
												    MegatronBertForSequenceClassification,
												    MegatronBertForTokenClassification,
												    MegatronBertModel,
												    MegatronBertPreTrainedModel,
												)

else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["input_features", "attention_mask"]







				def __init__( self    :       int			,					lowerCamelCase_    :       List[str]=8_0			,					lowerCamelCase_    :       Tuple=1_6_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       List[Any]=1_0			,					lowerCamelCase_    :       List[str]=2_5			,					lowerCamelCase_    :       List[Any]="hamming_window"			,					lowerCamelCase_    :       Tuple=3_2768.0			,					lowerCamelCase_    :       int=0.97			,					lowerCamelCase_    :       Optional[int]=1.0			,					lowerCamelCase_    :       Optional[Any]=True			,					lowerCamelCase_    :       Union[str, Any]=True			,					lowerCamelCase_    :       Optional[int]=False			,					**lowerCamelCase_    :       Optional[Any]			,					):

					"""simple docstring"""






					super().__init__(feature_size=lowerCamelCase_			,					sampling_rate=lowerCamelCase_			,					padding_value=lowerCamelCase_			,					**lowerCamelCase_						)

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

					_lowercase :    Optional[Any]							=							win_length * sampling_rate // 1_0_0_0
					_lowercase :    Tuple							=							hop_length * sampling_rate // 1_0_0_0

					_lowercase :    str							=							optimal_fft_length(self.sample_size						)
					_lowercase :    Dict							=							(self.n_fft // 2) + 1







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       np.array						):

					"""simple docstring"""






					if self.win_function == "hamming_window":
						_lowercase :    List[Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function			,					periodic=lowerCamelCase_						)
					else:
						_lowercase :    Union[str, Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function						)

					_lowercase :    Tuple							=							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 :    Tuple							=							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 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					if self.normalize_means:
						_lowercase :    Optional[int]							=							x[:input_length].mean(axis=0						)
						_lowercase :    int							=							np.subtract(lowerCamelCase_			,					lowerCamelCase_						)
					if self.normalize_vars:
						_lowercase :    int							=							x[:input_length].std(axis=0						)
						_lowercase :    Optional[Any]							=							np.divide(lowerCamelCase_			,					lowerCamelCase_						)

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

					# make sure array is in float32
					_lowercase :    Tuple							=							x.astype(np.floataa						)

					return x







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[np.ndarray]			,					lowerCamelCase_    :       Optional[np.ndarray] = None						):

					"""simple docstring"""






					_lowercase :    Dict							=							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    :       Dict			,					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_    :       Optional[int]			,					):

					"""simple docstring"""






					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 :    Optional[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}'''						)
					_lowercase :    Optional[int]							=							is_batched_numpy or (
					    isinstance(lowerCamelCase_			,					(list, tuple)						) and (isinstance(raw_speech[0]			,					(np.ndarray, tuple, list)						))
					)

					if is_batched:
						_lowercase :    str							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for speech in raw_speech]
					elif not is_batched and not isinstance(lowerCamelCase_			,					np.ndarray						):
						_lowercase :    Tuple							=							np.asarray(lowerCamelCase_			,					dtype=np.floataa						)
					elif isinstance(lowerCamelCase_			,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
						_lowercase :    Optional[int]							=							raw_speech.astype(np.floataa						)

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

					# extract fbank features
					_lowercase :    Optional[Any]							=							[self._extract_mfsc_features(lowerCamelCase_						) for one_waveform in raw_speech]

					# convert into correct format for padding
					_lowercase :    Optional[int]							=							BatchFeature({'input_features': features}						)

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

					_lowercase :    List[Any]							=							padded_inputs.get('attention_mask'						)
					if attention_mask is not None:
						_lowercase :    Union[str, Any]							=							[np.asarray(lowerCamelCase_			,					dtype=np.intaa						) for array in attention_mask]

					if self.normalize_means or self.normalize_vars:
						_lowercase :    int							=							(
						    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 :    List[Any]							=							self.normalize(
						    padded_inputs['input_features']			,					attention_mask=lowerCamelCase_						)

					if return_tensors is not None:
						_lowercase :    Union[str, Any]							=							padded_inputs.convert_to_tensors(lowerCamelCase_						)

					return padded_inputs



