



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     :  Dict					     =       logging.get_logger(__name__)

A     :  Dict					     =       '▁'

A     :  Tuple					     =       {'vocab_file': 'sentencepiece.bpe.model'}

A     :  List[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'
        ),
    }
}

A     :  Optional[int]					     =       {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}


class   lowerCamelCase					(_A					):
  """simple docstring"""







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







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]="<s>"  ,     __magic_name__ :       Tuple="</s>"  ,     __magic_name__ :       List[str]="</s>"  ,     __magic_name__ :       int="<s>"  ,     __magic_name__ :       Any="<unk>"  ,     __magic_name__ :       Any="<pad>"  ,     __magic_name__ :       Optional[Any]="<mask>"  ,     __magic_name__ :       Optional[Dict[str, Any]] = None  ,     **__magic_name__ :       Union[str, Any]  ,     )  ->	None:
        SCREAMING_SNAKE_CASE_          =  AddedToken(UpperCamelCase__  ,     lstrip=UpperCamelCase__  ,     rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__  ,     UpperCamelCase__   ) else mask_token

        SCREAMING_SNAKE_CASE_          =  {} if sp_model_kwargs is None else sp_model_kwargs

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

        SCREAMING_SNAKE_CASE_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(str(UpperCamelCase__   )   )
        SCREAMING_SNAKE_CASE_          =  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
        SCREAMING_SNAKE_CASE_          =  {"<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
        SCREAMING_SNAKE_CASE_          =  1

        SCREAMING_SNAKE_CASE_          =  len(self.sp_model   ) + self.fairseq_offset
        SCREAMING_SNAKE_CASE_          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(							self :       Tuple   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.__dict__.copy()
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self.sp_model.serialized_model_proto()
        return state







  def __setstate__(							self :       List[Any]  ,     __magic_name__ :       Dict   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  d

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

        SCREAMING_SNAKE_CASE_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )







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






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







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None  ,     __magic_name__ :       bool = False   )  ->	List[int]:






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

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







  def    __A     (							self :       str  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None   )  ->	List[int]:
        SCREAMING_SNAKE_CASE_          =  [self.sep_token_id]
        SCREAMING_SNAKE_CASE_          =  [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 :       List[str]   )  ->	str:
        return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  {self.convert_ids_to_tokens(UpperCamelCase__   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        return self.sp_model.encode(UpperCamelCase__  ,     out_type=UpperCamelCase__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       List[str]   )  ->	List[str]:






        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        SCREAMING_SNAKE_CASE_          =  self.sp_model.PieceToId(UpperCamelCase__   )

        # 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    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Any   )  ->	Union[str, 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    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       str   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  "".join(UpperCamelCase__   ).replace(UpperCamelCase__  ,     " "   ).strip()
        return out_string







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[str] = None   )  ->	Tuple[str]:






        if not os.path.isdir(UpperCamelCase__   ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
              return
        SCREAMING_SNAKE_CASE_          =  os.path.join(
            UpperCamelCase__  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase__   ) and os.path.isfile(self.vocab_file   ):
              copyfile(self.vocab_file  ,     UpperCamelCase__   )
        elif not os.path.isfile(self.vocab_file   ):
              with open(UpperCamelCase__  ,     "wb"   ) as fi:
                    SCREAMING_SNAKE_CASE_          =  self.sp_model.serialized_model_proto()
                    fi.write(UpperCamelCase__   )

        return (out_vocab_file,)



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


