



"""simple docstring"""






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


__snake_case							:     Dict   =     logging.get_logger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['input_features', 'is_longer']

	def __init__(  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Optional[int]=64 ,    _SCREAMING_SNAKE_CASE:	List[Any]=4_8000 ,    _SCREAMING_SNAKE_CASE:	str=480 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=10 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=1024 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.0 ,    _SCREAMING_SNAKE_CASE:	str=False ,    _SCREAMING_SNAKE_CASE:	float = 0 ,    _SCREAMING_SNAKE_CASE:	float = 1_4000 ,    _SCREAMING_SNAKE_CASE:	int = None ,    _SCREAMING_SNAKE_CASE:	str = "fusion" ,    _SCREAMING_SNAKE_CASE:	str = "repeatpad" ,    **_SCREAMING_SNAKE_CASE:	Tuple ,    )       -> str:

				"""simple docstring"""

				super().__init__(
				    feature_size=_a ,    sampling_rate=_a ,    padding_value=_a ,    return_attention_mask=_a ,    **_a ,    )
				__lowerCAmelCase   :						Tuple				= top_db
				__lowerCAmelCase   :						Tuple				= truncation
				__lowerCAmelCase   :						Optional[Any]				= padding
				__lowerCAmelCase   :						Optional[int]				= fft_window_size
				__lowerCAmelCase   :						Dict				= (fft_window_size >> 1) + 1
				__lowerCAmelCase   :						Any				= hop_length
				__lowerCAmelCase   :						List[Any]				= max_length_s
				__lowerCAmelCase   :						Tuple				= max_length_s * sampling_rate
				__lowerCAmelCase   :						Tuple				= sampling_rate
				__lowerCAmelCase   :						Optional[int]				= frequency_min
				__lowerCAmelCase   :						Tuple				= frequency_max
				__lowerCAmelCase   :						Tuple				= mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins ,    num_mel_filters=_a ,    min_frequency=_a ,    max_frequency=_a ,    sampling_rate=_a ,    norm=_a ,    mel_scale="htk" ,    )
				__lowerCAmelCase   :						Dict				= mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins ,    num_mel_filters=_a ,    min_frequency=_a ,    max_frequency=_a ,    sampling_rate=_a ,    norm="slaney" ,    mel_scale="slaney" ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= copy.deepcopy(self.__dict__)
				__lowerCAmelCase   :						Tuple				= self.__class__.__name__
				if "mel_filters" in output:
							del output["mel_filters"]
				if "mel_filters_slaney" in output:
							del output["mel_filters_slaney"]
				return output

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	np.array ,    _SCREAMING_SNAKE_CASE:	Optional[np.array] = None)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= spectrogram(
				    _a ,    window_function(self.fft_window_size ,    "hann") ,    frame_length=self.fft_window_size ,    hop_length=self.hop_length ,    power=2.0 ,    mel_filters=_a ,    log_mel="dB" ,    )
				return log_mel_spectrogram.T

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= np.array_split(list(range(0 ,    total_frames - chunk_frames + 1)) ,    3)
				if len(ranges[1]) == 0:
							# if the audio is too short, we just use the first chunk
							__lowerCAmelCase   :						List[Any]				= [0]
				if len(ranges[2]) == 0:
							# if the audio is too short, we just use the first chunk
							__lowerCAmelCase   :						int				= [0]
				# randomly choose index for each part
				__lowerCAmelCase   :						List[str]				= np.random.choice(ranges[0])
				__lowerCAmelCase   :						int				= np.random.choice(ranges[1])
				__lowerCAmelCase   :						Optional[int]				= np.random.choice(ranges[2])

				__lowerCAmelCase   :						Tuple				= mel[idx_front : idx_front + chunk_frames, :]
				__lowerCAmelCase   :						Dict				= mel[idx_middle : idx_middle + chunk_frames, :]
				__lowerCAmelCase   :						Dict				= mel[idx_back : idx_back + chunk_frames, :]

				__lowerCAmelCase   :						Optional[int]				= torch.tensor(mel[None, None, :])
				__lowerCAmelCase   :						Dict				= torch.nn.functional.interpolate(
				    _a ,    size=[chunk_frames, 64] ,    mode="bilinear" ,    align_corners=_a)
				__lowerCAmelCase   :						str				= mel_shrink[0][0].numpy()
				__lowerCAmelCase   :						Tuple				= np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,    axis=0)
				return mel_fusion

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	np.array ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Tuple:

				"""simple docstring"""

				if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
										__lowerCAmelCase   :						Dict				= True
										# random crop to max_length (for compatibility) -> this should be handled by self.pad
										__lowerCAmelCase   :						Tuple				= len(_a) - max_length
										__lowerCAmelCase   :						Optional[int]				= np.random.randint(0 ,    overflow + 1)
										__lowerCAmelCase   :						List[Any]				= waveform[idx : idx + max_length]
										__lowerCAmelCase   :						Optional[Any]				= self._np_extract_fbank_features(_a ,    self.mel_filters_slaney)[None, :]
							elif truncation == "fusion":
										__lowerCAmelCase   :						Dict				= self._np_extract_fbank_features(_a ,    self.mel_filters)
										__lowerCAmelCase   :						Tuple				= max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
										__lowerCAmelCase   :						Optional[int]				= mel.shape[0]
										if chunk_frames == total_frames:
													# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
													# In this case, we just use the whole audio.
													__lowerCAmelCase   :						Optional[int]				= np.stack([mel, mel, mel, mel] ,    axis=0)
													__lowerCAmelCase   :						str				= False
										else:
													__lowerCAmelCase   :						str				= self._random_mel_fusion(_a ,    _a ,    _a)
													__lowerCAmelCase   :						Optional[Any]				= True
							else:
										raise NotImplementedError(F"""data_truncating {truncation} not implemented""")

				else:
							__lowerCAmelCase   :						Optional[int]				= False
							# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
							if waveform.shape[0] < max_length:
										if padding == "repeat":
													__lowerCAmelCase   :						int				= int(max_length / len(_a))
													__lowerCAmelCase   :						Any				= np.stack(np.tile(_a ,    n_repeat + 1))[:max_length]
										if padding == "repeatpad":
													__lowerCAmelCase   :						List[str]				= int(max_length / len(_a))
													__lowerCAmelCase   :						Optional[Any]				= np.stack(np.tile(_a ,    _a))
										__lowerCAmelCase   :						Any				= np.pad(_a ,    (0, max_length - waveform.shape[0]) ,    mode="constant" ,    constant_values=0)

							if truncation == "fusion":
										__lowerCAmelCase   :						List[Any]				= self._np_extract_fbank_features(_a ,    self.mel_filters)
										__lowerCAmelCase   :						Optional[Any]				= np.stack([input_mel, input_mel, input_mel, input_mel] ,    axis=0)
							else:
										__lowerCAmelCase   :						Union[str, Any]				= self._np_extract_fbank_features(_a ,    self.mel_filters_slaney)[None, :]

				return input_mel, longer







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

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= truncation if truncation is not None else self.truncation
				__lowerCAmelCase   :						List[Any]				= padding if padding else self.padding

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
										    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
										    F""" was sampled with {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(_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   :						Optional[int]				= [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   :						Tuple				= raw_speech.astype(np.floataa)

				# always return batch
				if not is_batched:
							__lowerCAmelCase   :						Any				= [np.asarray(_a)]

				# convert to mel spectrogram, truncate and pad if needed.
				__lowerCAmelCase   :						str				= [
				    self._get_input_mel(_a ,    max_length if max_length else self.nb_max_samples ,    _a ,    _a)
				    for waveform in raw_speech
				]

				__lowerCAmelCase   :						int				= []
				__lowerCAmelCase   :						Any				= []
				for mel, longer in padded_inputs:
							input_mel.append(_a)
							is_longer.append(_a)

				if truncation == "fusion" and sum(_a) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							__lowerCAmelCase   :						List[Any]				= np.random.randint(0 ,    len(_a))
							__lowerCAmelCase   :						List[str]				= True

				if isinstance(input_mel[0] ,    _a):
							__lowerCAmelCase   :						Tuple				= [np.asarray(_a ,    dtype=np.floataa) for feature in input_mel]

				# is_longer is a list of bool
				__lowerCAmelCase   :						List[str]				= [[longer] for longer in is_longer]

				__lowerCAmelCase   :						Optional[Any]				= {"""input_features""": input_mel, """is_longer""": is_longer}
				__lowerCAmelCase   :						int				= BatchFeature(_a)

				if return_tensors is not None:
							__lowerCAmelCase   :						int				= input_features.convert_to_tensors(_a)

				return input_features




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale