






'''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


UpperCamelCase     :   Any	      =		logging.get_logger(__name__)




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       ['input_features', 'is_longer']






		def __init__(   self ,_lowerCAmelCase=64 ,_lowerCAmelCase=4_80_00 ,_lowerCAmelCase=4_80 ,_lowerCAmelCase=10 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=False ,_lowerCAmelCase = 0 ,_lowerCAmelCase = 1_40_00 ,_lowerCAmelCase = None ,_lowerCAmelCase = "fusion" ,_lowerCAmelCase = "repeatpad" ,**_lowerCAmelCase ,):
								super().__init__(
								    feature_size=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,padding_value=_lowerCAmelCase ,return_attention_mask=_lowerCAmelCase ,**_lowerCAmelCase ,)
								lowerCamelCase__								=	top_db
								lowerCamelCase__								=	truncation
								lowerCamelCase__								=	padding
								lowerCamelCase__								=	fft_window_size
								lowerCamelCase__								=	(fft_window_size >> 1) + 1
								lowerCamelCase__								=	hop_length
								lowerCamelCase__								=	max_length_s
								lowerCamelCase__								=	max_length_s * sampling_rate
								lowerCamelCase__								=	sampling_rate
								lowerCamelCase__								=	frequency_min
								lowerCamelCase__								=	frequency_max
								lowerCamelCase__								=	mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_lowerCAmelCase ,min_frequency=_lowerCAmelCase ,max_frequency=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,norm=_lowerCAmelCase ,mel_scale="""htk""" ,)
								lowerCamelCase__								=	mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_lowerCAmelCase ,min_frequency=_lowerCAmelCase ,max_frequency=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,norm="""slaney""" ,mel_scale="""slaney""" ,)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	copy.deepcopy(self.__dict__							)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								lowerCamelCase__								=	spectrogram(
								    _lowerCAmelCase ,window_function(self.fft_window_size ,"""hann"""							) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_lowerCAmelCase ,log_mel="""dB""" ,)
								return log_mel_spectrogram.T






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	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__								=	[0]
								if len(ranges[2]							) == 0:
														# if the audio is too short, we just use the first chunk
														lowerCamelCase__								=	[0]
								# randomly choose index for each part
								lowerCamelCase__								=	np.random.choice(ranges[0]							)
								lowerCamelCase__								=	np.random.choice(ranges[1]							)
								lowerCamelCase__								=	np.random.choice(ranges[2]							)

								lowerCamelCase__								=	mel[idx_front : idx_front + chunk_frames, :]
								lowerCamelCase__								=	mel[idx_middle : idx_middle + chunk_frames, :]
								lowerCamelCase__								=	mel[idx_back : idx_back + chunk_frames, :]

								lowerCamelCase__								=	torch.tensor(mel[None, None, :]							)
								lowerCamelCase__								=	torch.nn.functional.interpolate(
								    _lowerCAmelCase ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_lowerCAmelCase							)
								lowerCamelCase__								=	mel_shrink[0][0].numpy()
								lowerCamelCase__								=	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0							)
								return mel_fusion






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								if waveform.shape[0] > max_length:
														if truncation == "rand_trunc":
																				lowerCamelCase__								=	True
																				# random crop to max_length (for compatibility) -> this should be handled by self.pad
																				lowerCamelCase__								=	len(_lowerCAmelCase							) - max_length
																				lowerCamelCase__								=	np.random.randint(0 ,overflow + 1							)
																				lowerCamelCase__								=	waveform[idx : idx + max_length]
																				lowerCamelCase__								=	self._np_extract_fbank_features(_lowerCAmelCase ,self.mel_filters_slaney							)[None, :]
														elif truncation == "fusion":
																				lowerCamelCase__								=	self._np_extract_fbank_features(_lowerCAmelCase ,self.mel_filters							)
																				lowerCamelCase__								=	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																				lowerCamelCase__								=	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__								=	np.stack([mel, mel, mel, mel] ,axis=0							)
																										lowerCamelCase__								=	False
																				else:
																										lowerCamelCase__								=	self._random_mel_fusion(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)
																										lowerCamelCase__								=	True
														else:
																				raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

								else:
														lowerCamelCase__								=	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(max_length / len(_lowerCAmelCase							)							)
																										lowerCamelCase__								=	np.stack(np.tile(_lowerCAmelCase ,n_repeat + 1							)							)[:max_length]
																				if padding == "repeatpad":
																										lowerCamelCase__								=	int(max_length / len(_lowerCAmelCase							)							)
																										lowerCamelCase__								=	np.stack(np.tile(_lowerCAmelCase ,_lowerCAmelCase							)							)
																				lowerCamelCase__								=	np.pad(_lowerCAmelCase ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0							)

														if truncation == "fusion":
																				lowerCamelCase__								=	self._np_extract_fbank_features(_lowerCAmelCase ,self.mel_filters							)
																				lowerCamelCase__								=	np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0							)
														else:
																				lowerCamelCase__								=	self._np_extract_fbank_features(_lowerCAmelCase ,self.mel_filters_slaney							)[None, :]

								return input_mel, longer






		def __call__(   self ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):
								lowerCamelCase__								=	truncation if truncation is not None else self.truncation
								lowerCamelCase__								=	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__								=	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}'''							)
								lowerCamelCase__								=	is_batched_numpy or (
								    isinstance(_lowerCAmelCase ,(list, tuple)							) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list)							))
								)

								if is_batched:
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.floataa							) for speech in raw_speech]
								elif not is_batched and not isinstance(_lowerCAmelCase ,np.ndarray							):
														lowerCamelCase__								=	np.asarray(_lowerCAmelCase ,dtype=np.floataa							)
								elif isinstance(_lowerCAmelCase ,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(_lowerCAmelCase							)]

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

								lowerCamelCase__								=	[]
								lowerCamelCase__								=	[]
								for mel, longer in padded_inputs:
														input_mel.append(_lowerCAmelCase							)
														is_longer.append(_lowerCAmelCase							)

								if truncation == "fusion" and sum(_lowerCAmelCase							) == 0:
														# if no audio is longer than 10s, then randomly select one audio to be longer
														lowerCamelCase__								=	np.random.randint(0 ,len(_lowerCAmelCase							)							)
														lowerCamelCase__								=	True

								if isinstance(input_mel[0] ,_lowerCAmelCase							):
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.floataa							) for feature in input_mel]

								# is_longer is a list of bool
								lowerCamelCase__								=	[[longer] for longer in is_longer]

								lowerCamelCase__								=	{"""input_features""": input_mel, """is_longer""": is_longer}
								lowerCamelCase__								=	BatchFeature(_lowerCAmelCase							)

								if return_tensors is not None:
														lowerCamelCase__								=	input_features.convert_to_tensors(_lowerCAmelCase							)

								return input_features



"""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


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'sentencepiece.bpe.model'}

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

__SCREAMING_SNAKE_CASE							=     {
    'camembert-base': 512,
}

__SCREAMING_SNAKE_CASE							=     '▁'





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      ['''input_ids''', '''attention_mask''']



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







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      UpperCamelCase_     :					List[str]   	={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  additional_special_tokens=_lowerCamelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(_lowerCamelCase   )   )
      UpperCamelCase_     :					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>
      UpperCamelCase_     :					Tuple   	={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      UpperCamelCase_     :					List[str]   	=len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					List[str]   	=len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					Any   	={v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''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_     :					str   	=[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase      ,  token_ids_a=_lowerCamelCase      ,  already_has_special_tokens=_lowerCamelCase   )

      if token_ids_a is None:
       return [1] + ([0] * len(_lowerCamelCase   )) + [1]
      return [1] + ([0] * len(_lowerCamelCase   )) + [1, 1] + ([0] * len(_lowerCamelCase   )) + [1]



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					Dict   	=[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  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return self.sp_model.encode(_lowerCamelCase      ,  out_type=_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(_lowerCamelCase   ) == 0:
       # Convert sentence piece unk token to fairseq unk token index
       return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[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  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[]
      UpperCamelCase_     :					Optional[Any]   	=''
      UpperCamelCase_     :					Optional[Any]   	=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(_lowerCamelCase   ) + token
        UpperCamelCase_     :					Union[str, Any]   	=True
        UpperCamelCase_     :					Optional[Any]   	=[]
       else:
        current_sub_tokens.append(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=False
      out_string += self.sp_model.decode(_lowerCamelCase   )
      return out_string.strip()



     def __getstate__(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.__dict__.copy()
      UpperCamelCase_     :					str   	=None
      return state



     def __setstate__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=d

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

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




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      if not os.path.isdir(_lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
      UpperCamelCase_     :					Any   	=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   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file      ,  _lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
       with open(_lowerCamelCase      ,  'wb'   ) as fi:
        UpperCamelCase_     :					int   	=self.sp_model.serialized_model_proto()
        fi.write(_lowerCamelCase   )

      return (out_vocab_file,)
