


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       :       Optional[Any]						       =				logging.get_logger(__name__)

class       __magic_name__	(     __lowerCAmelCase):
					A:		List[str]			=      ["input_features", "attention_mask"]


					def __init__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]=80							,	lowerCamelCase__							:     int=16000							,	lowerCamelCase__							:     Optional[int]=0.0							,	lowerCamelCase__							:     Dict=10							,	lowerCamelCase__							:     Tuple=25							,	lowerCamelCase__							:     List[Any]="hamming_window"							,	lowerCamelCase__							:     Tuple=32768.0							,	lowerCamelCase__							:     Optional[int]=0.97							,	lowerCamelCase__							:     int=1.0							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     str=False							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      Any:





						'''simple docstring'''






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

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

						UpperCamelCase__					:    Any          =   win_length * sampling_rate // 1000
						UpperCamelCase__					:    Tuple          =   hop_length * sampling_rate // 1000

						UpperCamelCase__					:    Union[str, Any]          =   optimal_fft_length(self.sample_size			)
						UpperCamelCase__					:    Tuple          =   (self.n_fft // 2) + 1


					def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     np.array			)  ->      np.ndarray:





						'''simple docstring'''






						if self.win_function == "hamming_window":
							UpperCamelCase__					:    Tuple          =   window_function(window_length=self.sample_size							,	name=self.win_function							,	periodic=lowerCamelCase__			)
						else:
							UpperCamelCase__					:    Union[str, Any]          =   window_function(window_length=self.sample_size							,	name=self.win_function			)

						UpperCamelCase__					:    List[str]          =   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							,	)

						UpperCamelCase__					:    int          =   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							:     Tuple							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     str			)  ->      Optional[Any]:





						'''simple docstring'''






						if self.normalize_means:
							UpperCamelCase__					:    Union[str, Any]          =   x[:input_length].mean(axis=0			)
							UpperCamelCase__					:    str          =   np.subtract(lowerCamelCase__							,	lowerCamelCase__			)
						if self.normalize_vars:
							UpperCamelCase__					:    Any          =   x[:input_length].std(axis=0			)
							UpperCamelCase__					:    List[Any]          =   np.divide(lowerCamelCase__							,	lowerCamelCase__			)

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

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

						return x


					def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     List[np.ndarray]							,	lowerCamelCase__							:     Optional[np.ndarray] = None			)  ->      List[np.ndarray]:





						'''simple docstring'''






						UpperCamelCase__					:    Tuple          =   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							:     Optional[Any]							,	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__							:     str							,	)  ->      BatchFeature:





						'''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.'''			)

						UpperCamelCase__					:    List[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}"			)
						UpperCamelCase__					:    str          =   is_batched_numpy or (
						    isinstance(lowerCamelCase__							,	(list, tuple)			) and (isinstance(raw_speech[0]							,	(np.ndarray, tuple, list)			))
						)

						if is_batched:
							UpperCamelCase__					:    List[Any]          =   [np.asarray(lowerCamelCase__							,	dtype=np.floataa			) for speech in raw_speech]
						elif not is_batched and not isinstance(lowerCamelCase__							,	np.ndarray			):
							UpperCamelCase__					:    Any          =   np.asarray(lowerCamelCase__							,	dtype=np.floataa			)
						elif isinstance(lowerCamelCase__							,	np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
							UpperCamelCase__					:    Tuple          =   raw_speech.astype(np.floataa			)

						# always return batch
						if not is_batched:
							UpperCamelCase__					:    Optional[Any]          =   [raw_speech]

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

						# convert into correct format for padding
						UpperCamelCase__					:    Union[str, Any]          =   BatchFeature({'''input_features''': features}			)

						UpperCamelCase__					:    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
						UpperCamelCase__					:    Union[str, Any]          =   padded_inputs.get('''input_features'''			)
						if isinstance(input_features[0]							,	lowerCamelCase__			):
							UpperCamelCase__					:    Optional[int]          =   [np.asarray(lowerCamelCase__							,	dtype=np.floataa			) for feature in input_features]

						UpperCamelCase__					:    Dict          =   padded_inputs.get('''attention_mask'''			)
						if attention_mask is not None:
							UpperCamelCase__					:    Optional[Any]          =   [np.asarray(lowerCamelCase__							,	dtype=np.intaa			) for array in attention_mask]

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

						if return_tensors is not None:
							UpperCamelCase__					:    Tuple          =   padded_inputs.convert_to_tensors(lowerCamelCase__			)

						return padded_inputs






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


