



'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		List[str]		      = ['input_features', 'attention_mask']


	def __init__(self	,				lowercase=80	,				lowercase=16000	,				lowercase=80	,				lowercase=0.0	,				lowercase=True	,				lowercase=True	,				lowercase=True	,				**lowercase	,				):
				super().__init__(feature_size=lowercase	,				sampling_rate=lowercase	,				padding_value=lowercase	,				**lowercase							)
				A_     :						int     =					num_mel_bins
				A_     :						Tuple     =					do_ceptral_normalize
				A_     :						Optional[int]     =					normalize_means
				A_     :						List[str]     =					normalize_vars
				A_     :						Any     =					True


	def    _a     (self	,				lowercase	,				):
				A_     :						Tuple     =					waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
				A_     :						List[str]     =					torch.from_numpy(lowercase							).unsqueeze(0							)
				A_     :						Optional[Any]     =					ta_kaldi.fbank(lowercase	,				num_mel_bins=self.num_mel_bins	,				sample_frequency=self.sampling_rate							)
				return features.numpy()


	@staticmethod
	def    _a     (lowercase	,				lowercase	,				lowercase = True	,				lowercase = True	,				lowercase = 0.0	,				):
				# make sure we normalize float32 arrays
				if normalize_means:
							A_     :						List[Any]     =					x[:input_length].mean(axis=0							)
							A_     :						Union[str, Any]     =					np.subtract(lowercase	,				lowercase							)
				if normalize_vars:
							A_     :						Dict     =					x[:input_length].std(axis=0							)
							A_     :						Optional[int]     =					np.divide(lowercase	,				lowercase							)

				if input_length < x.shape[0]:
							A_     :						Optional[int]     =					padding_value

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

				return x


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						List[Any]     =					attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [
				    self.utterance_cmvn(lowercase	,				lowercase	,				self.normalize_means	,				self.normalize_vars	,				self.padding_value							)
				    for x, n in zip(lowercase	,				lowercase							)
				]


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

				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."""							)

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

				if is_batched:
							A_     :						List[Any]     =					[np.asarray(lowercase	,				dtype=np.floataa							) for speech in raw_speech]
				elif not is_batched and not isinstance(lowercase	,				np.ndarray							):
							A_     :						Union[str, Any]     =					np.asarray(lowercase	,				dtype=np.floataa							)
				elif isinstance(lowercase	,				np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
							A_     :						List[str]     =					raw_speech.astype(np.floataa							)

				# always return batch
				if not is_batched:
							A_     :						Union[str, Any]     =					[raw_speech]

				# extract fbank features
				A_     :						List[Any]     =					[self._extract_fbank_features(lowercase							) for waveform in raw_speech]

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

				A_     :						List[str]     =					self.pad(
				    lowercase	,				padding=lowercase	,				max_length=lowercase	,				truncation=lowercase	,				pad_to_multiple_of=lowercase	,				return_attention_mask=lowercase	,				**lowercase	,				)

				# make sure list is in array format
				A_     :						str     =					padded_inputs.get("""input_features"""							)
				if isinstance(input_features[0]	,				lowercase							):
							A_     :						Tuple     =					[np.asarray(lowercase	,				dtype=np.floataa							) for feature in input_features]

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

				# Utterance-level cepstral mean and variance normalization
				if self.do_ceptral_normalize:
							A_     :						Tuple     =					(
							    np.array(lowercase	,				dtype=np.intaa							)
							    if self._get_padding_strategies(lowercase	,				max_length=lowercase							) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							A_     :						int     =					self.normalize(
							    padded_inputs["""input_features"""]	,				attention_mask=lowercase							)

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

				return padded_inputs




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)