





"""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


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_									={
    """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"""
        ),
    },
}

UpperCamelCase_									={
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

UpperCamelCase_									="""▁"""




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   ['''input_ids''', '''attention_mask''']






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else mask_token

										_UpperCamelCase      : int         =   {} 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__,			sp_model_kwargs=self.sp_model_kwargs,			**lowerCAmelCase__,			)

										_UpperCamelCase      : Union[str, Any]         =   vocab_file
										_UpperCamelCase      : Union[str, Any]         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(lowerCAmelCase__							)							)

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

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






				def     snake_case     (			self		:   List[str],			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      : Union[str, Any]         =   [self.cls_token_id]
										_UpperCamelCase      : Dict         =   [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None,			lowerCAmelCase__		:   bool = False							)     ->				List[int]:




										'''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     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   [self.sep_token_id]
										_UpperCamelCase      : Any         =   [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     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										return len(self.sp_model							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   {self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				List[str]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										_UpperCamelCase      : Optional[int]         =   self.sp_model.PieceToId(lowerCAmelCase__							)

										return spm_id if spm_id else self.unk_token_id






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[str]							)     ->				Optional[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[int]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   []
										_UpperCamelCase      : str         =   ''''''
										_UpperCamelCase      : Dict         =   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      : Optional[int]         =   True
																						_UpperCamelCase      : Tuple         =   []
																else:
																						current_sub_tokens.append(lowerCAmelCase__							)
																						_UpperCamelCase      : str         =   False
										out_string += self.sp_model.decode(lowerCAmelCase__							)
										return out_string.strip()






				def __getstate__(			self		:   Tuple							)     ->				Optional[int]:




										'''simple docstring'''



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






				def __setstate__(			self		:   Any,			lowerCAmelCase__		:   List[Any]							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   d

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

										_UpperCamelCase      : int         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										if not os.path.isdir(lowerCAmelCase__							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										_UpperCamelCase      : Optional[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      : Any         =   self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase__							)

										return (out_vocab_file,)










"""simple docstring"""




from __future__ import annotations

from random import choice





def 	a_    (							_lowercase       ):
						return choice(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   random_pivot(_lowercase       )

						# partition based on pivot
						# linear time
						_UpperCamelCase      : Union[str, Any]         =   [e for e in lst if e < pivot]
						_UpperCamelCase      : int         =   [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(_lowercase       ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(_lowercase       ) < k - 1:
												return kth_number(_lowercase	,						k - len(_lowercase       ) - 1       )
						# pivot is in elements smaller than k
						else:
												return kth_number(_lowercase	,						_lowercase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



