






"""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       :	Dict             = {'vocab_file': 'sentencepiece.bpe.model'}

__a       :	Any             = {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__a       :	Dict             = {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__a       :	int             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =['input_ids', 'attention_mask']




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

												'''simple docstring'''

												a__          =  AddedToken(__A				,		lstrip=__A				,		rstrip=__A    ) if isinstance(__A				,		__A    ) else mask_token

												a__          =  {} 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				,		sp_model_kwargs=self.sp_model_kwargs				,		**__A				,		)

												a__          =  vocab_file
												a__          =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(str(__A    )    )

												a__          =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												a__          =  len(self.sp_model    ) - 1
												a__          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def  lowercase		(   self:     Tuple				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Tuple				,		__A:     List[int]				,		__A:     Optional[List[int]] = None				,		__A:     bool = False    ):

												'''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  lowercase		(   self:     str				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												return len(self.sp_model    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
												vocab.update(self.added_tokens_encoder    )
												return vocab




							def  lowercase		(   self:     Optional[int]				,		__A:     str    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Dict				,		__A:     Optional[int]    ):

												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												a__          =  self.sp_model.PieceToId(__A    )

												return spm_id if spm_id else self.unk_token_id




							def  lowercase		(   self:     str				,		__A:     List[str]    ):

												'''simple docstring'''

												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(__A    )




							def  lowercase		(   self:     Dict				,		__A:     int    ):

												'''simple docstring'''

												a__          =  []
												a__          =  ''''''
												a__          =  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
																						a__          =  True
																						a__          =  []
																	else:
																						current_sub_tokens.append(__A    )
																						a__          =  False
												out_string += self.sp_model.decode(__A    )
												return out_string.strip()




							def __getstate__(   self:     int    ):

												'''simple docstring'''

												a__          =  self.__dict__.copy()
												a__          =  None
												return state




							def __setstate__(   self:     Union[str, Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  d

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

												a__          =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(self.vocab_file    )





							def  lowercase		(   self:     Optional[int]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  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:
																						a__          =  self.sp_model.serialized_model_proto()
																						fi.write(__A    )

												return (out_vocab_file,)










"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

												if self._in_target_context_manager:
																	return self.current_processor(*__A				,		**__A    )

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio is None and text is None:
																	raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

												if text is not None:
																	a__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	a__          =  audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						a__          =  audio_inputs['''padding_mask''']
																	return inputs




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio_values is not None:
																	return self._decode_audio(__A				,		padding_mask=__A    )
												else:
																	return self.tokenizer.batch_decode(*__A				,		**__A    )




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

												return self.tokenizer.decode(*__A				,		**__A    )





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

												# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
												# token (so that the generated audio values are **not** treated as padded tokens)
												a__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

												a__          =  audio_values.tolist()
												for i in range(__A    ):
																	a__          =  np.asarray(audio_values[i]    )[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	a__          =  sliced_audio.reshape(__A				,		-1    )

												return audio_values


