


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

__UpperCAmelCase    :Union[str, Any]				       =     "▁"

__UpperCAmelCase    :int				       =     {"vocab_file": "sentencepiece.bpe.model"}

__UpperCAmelCase    :Any				       =     {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__UpperCAmelCase    :Optional[Any]				       =     {
    "xlm-roberta-base": 5_1_2,
    "xlm-roberta-large": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-english": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-german": 5_1_2,
}



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE :				List[str]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE :				List[str]							     =     ["input_ids", "attention_mask"]

     def __init__(       self  :	List[Any]							,				snake_case  :	int							,				snake_case  :	Optional[int]="<s>"							,				snake_case  :	int="</s>"							,				snake_case  :	List[Any]="</s>"							,				snake_case  :	str="<s>"							,				snake_case  :	Any="<unk>"							,				snake_case  :	Any="<pad>"							,				snake_case  :	Optional[Any]="<mask>"							,				snake_case  :	Optional[Dict[str, Any]] = None							,				**snake_case  :	Any							,				)		->							None:
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCAmelCase			:				Tuple													=    AddedToken(snake_case							,				lstrip=snake_case							,				rstrip=snake_case    ) if isinstance(snake_case							,				snake_case    ) else mask_token

          __UpperCAmelCase			:				str													=    {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=snake_case							,				eos_token=snake_case							,				unk_token=snake_case							,				sep_token=snake_case							,				cls_token=snake_case							,				pad_token=snake_case							,				mask_token=snake_case							,				sp_model_kwargs=self.sp_model_kwargs							,				**snake_case							,				)

          __UpperCAmelCase			:				Tuple													=    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.Load(str(snake_case    )    )
          __UpperCAmelCase			:				List[str]													=    vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          __UpperCAmelCase			:				Tuple													=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          __UpperCAmelCase			:				Optional[Any]													=    1

          __UpperCAmelCase			:				Optional[Any]													=    len(self.sp_model    ) + self.fairseq_offset
          __UpperCAmelCase			:				List[Any]													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(       self  :	Dict    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Optional[int]													=    self.__dict__.copy()
          __UpperCAmelCase			:				str													=    None
          __UpperCAmelCase			:				int													=    self.sp_model.serialized_model_proto()
          return state

     def __setstate__(       self  :	List[Any]							,				snake_case  :	Any    )		->							Optional[int]:
          __UpperCAmelCase			:				Tuple													=    d

          # for backward compatibility
          if not hasattr(self							,				'''sp_model_kwargs'''    ):
               __UpperCAmelCase			:				List[Any]													=    {}

          __UpperCAmelCase			:				Tuple													=    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )

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

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

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

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

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

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

     @property
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							str:
          return len(self.sp_model    ) + self.fairseq_offset + 1  # Add the <mask> token

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Optional[int]													=    {self.convert_ids_to_tokens(snake_case    ): i for i in range(self.vocab_size    )}
          vocab.update(self.added_tokens_encoder    )
          return vocab

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	str    )		->							List[str]:
          return self.sp_model.encode(snake_case							,				out_type=snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Dict    )		->							List[Any]:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          __UpperCAmelCase			:				Any													=    self.sp_model.PieceToId(snake_case    )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	List[str]    )		->							Optional[Any]:
          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  :	Optional[Any]							,				snake_case  :	Union[str, Any]    )		->							Dict:
          __UpperCAmelCase			:				str													=    ''''''.join(snake_case    ).replace(snake_case							,				''' '''    ).strip()
          return out_string






     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	str							,				snake_case  :	Optional[str] = None    )		->							Tuple[str]:
          if not os.path.isdir(snake_case    ):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
               return
          __UpperCAmelCase			:				List[str]													=    os.path.join(
              snake_case							,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

          return (out_vocab_file,)



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			list						):





     '''simple docstring'''






     if not nums:
          raise ValueError('''List is empty'''						)
     return sum(_lowercase						) / len(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()