
"""simple docstring"""






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

import numpy as np

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


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( SCREAMING_SNAKE_CASE_		):
			lowercase        =     ['''input_features''']






			def __init__(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	List[str]=80      ,SCREAMING_SNAKE_CASE_  :	Dict=16_000      ,SCREAMING_SNAKE_CASE_  :	int=160      ,SCREAMING_SNAKE_CASE_  :	List[Any]=30      ,SCREAMING_SNAKE_CASE_  :	Dict=400      ,SCREAMING_SNAKE_CASE_  :	Dict=0.0      ,SCREAMING_SNAKE_CASE_  :	Dict=False      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,)       ->	List[Any]:





				"""simple docstring"""


				super().__init__(
				    feature_size=__a      ,sampling_rate=__a      ,padding_value=__a      ,return_attention_mask=__a      ,**__a      ,)
				lowerCAmelCase												=  n_fft
				lowerCAmelCase												=  hop_length
				lowerCAmelCase												=  chunk_length
				lowerCAmelCase												=  chunk_length * sampling_rate
				lowerCAmelCase												=  self.n_samples // hop_length
				lowerCAmelCase												=  sampling_rate
				lowerCAmelCase												=  mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2      ,num_mel_filters=__a      ,min_frequency=0.0      ,max_frequency=8_000.0      ,sampling_rate=__a      ,norm='''slaney'''      ,mel_scale='''slaney'''      ,)






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	np.array			)       ->	np.ndarray:





				"""simple docstring"""


				lowerCAmelCase												=  spectrogram(
				    __a      ,window_function(self.n_fft      ,'''hann'''			)      ,frame_length=self.n_fft      ,hop_length=self.hop_length      ,power=2.0      ,mel_filters=self.mel_filters      ,log_mel='''log10'''      ,)
				lowerCAmelCase												=  log_spec[:, :-1]
				lowerCAmelCase												=  np.maximum(__a      ,log_spec.max() - 8.0			)
				lowerCAmelCase												=  (log_spec + 4.0) / 4.0
				return log_spec






			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 					UpperCAmelCase							(SCREAMING_SNAKE_CASE_  :	List[np.ndarray]      ,SCREAMING_SNAKE_CASE_  :	List[np.ndarray]      ,SCREAMING_SNAKE_CASE_  :	float = 0.0			)       ->	List[np.ndarray]:





				"""simple docstring"""


				if attention_mask is not None:
					lowerCAmelCase												=  np.array(__a      ,np.intaa			)
					lowerCAmelCase												=  []

					for vector, length in zip(__a      ,attention_mask.sum(-1			)			):
						lowerCAmelCase												=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
						if length < normed_slice.shape[0]:
							lowerCAmelCase												=  padding_value

						normed_input_values.append(__a			)
				else:
					lowerCAmelCase												=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7			) for x in input_values]

				return normed_input_values






			def __call__(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[str] = "max_length"      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,**SCREAMING_SNAKE_CASE_  :	List[Any]      ,)       ->	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.__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												=  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												=  is_batched_numpy or (
				    isinstance(__a      ,(list, tuple)			) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)			))
				)

				if is_batched:
					lowerCAmelCase												=  [np.asarray([speech]      ,dtype=np.floataa			).T for speech in raw_speech]
				elif not is_batched and not isinstance(__a      ,np.ndarray			):
					lowerCAmelCase												=  np.asarray(__a      ,dtype=np.floataa			)
				elif isinstance(__a      ,np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
					lowerCAmelCase												=  raw_speech.astype(np.floataa			)

				# always return batch
				if not is_batched:
					lowerCAmelCase												=  [np.asarray([raw_speech]			).T]

				lowerCAmelCase												=  BatchFeature({'''input_features''': raw_speech}			)

				# convert into correct format for padding

				lowerCAmelCase												=  self.pad(
				    __a      ,padding=__a      ,max_length=max_length if max_length else self.n_samples      ,truncation=__a      ,pad_to_multiple_of=__a      ,return_attention_mask=return_attention_mask or do_normalize      ,)

				# zero-mean and unit-variance normalization
				if do_normalize:
					lowerCAmelCase												=  self.zero_mean_unit_var_norm(
					    padded_inputs['''input_features''']      ,attention_mask=padded_inputs['''attention_mask''']      ,padding_value=self.padding_value      ,)
					lowerCAmelCase												=  np.stack(padded_inputs['''input_features''']      ,axis=0			)

				# make sure list is in array format
				lowerCAmelCase												=  padded_inputs.get('''input_features'''			).transpose(2      ,0      ,1			)

				lowerCAmelCase												=  [self._np_extract_fbank_features(__a			) for waveform in input_features[0]]

				if isinstance(input_features[0]      ,__a			):
					lowerCAmelCase												=  [np.asarray(__a      ,dtype=np.floataa			) for feature in input_features]
				else:
					lowerCAmelCase												=  input_features

				if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					lowerCAmelCase												=  padded_inputs['attention_mask'][:, :: self.hop_length]

				if return_tensors is not None:
					lowerCAmelCase												=  padded_inputs.convert_to_tensors(__a			)

				return padded_inputs






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





				"""simple docstring"""


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







'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__    :   Tuple           =					logging.get_logger(__name__)

A__    :   int           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__a      ,     eos_token=__a      ,     unk_token=__a      ,     sep_token=__a      ,     cls_token=__a      ,     pad_token=__a      ,     mask_token=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__a      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							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]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       if token_ids_a is None:
             return [1] + ([0] * len(__a  )) + [1]
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [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]
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__a  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(__a  ) + token
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Dict													=    {}

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Dict													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



