





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__  			="""▁"""

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

UpperCamelCase__  			={
    """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__  			={
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}





class       lowerCAmelCase__(			a_     ):






       '''simple docstring'''


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




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

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

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

            _SCREAMING_SNAKE_CASE							:						List[Any]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
            self.sp_model.Load(str(__SCREAMING_SNAKE_CASE				)				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				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							:						Optional[int]         =				{"<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							:						Any         =				1

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




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




       def __setstate__(   self     ,    __lowerCamelCase				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						str         =				d

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

            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase = None				)      ->  str:

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase = None     ,    __lowerCamelCase = False				)      ->  str:

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

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase = None				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				[self.sep_token_id]
            _SCREAMING_SNAKE_CASE							:						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      UpperCamelCase_      (   self				)      ->  str:
            return len(self.sp_model				) + self.fairseq_offset + 1  # Add the <mask> token




       def      UpperCamelCase_      (   self				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						int         =				{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
            vocab.update(self.added_tokens_encoder				)
            return vocab




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  str:
            return self.sp_model.encode(__SCREAMING_SNAKE_CASE     ,    out_type=__SCREAMING_SNAKE_CASE				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  int:
            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            _SCREAMING_SNAKE_CASE							:						Any         =				self.sp_model.PieceToId(__SCREAMING_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      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  List[str]:
            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      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Tuple         =				"".join(__SCREAMING_SNAKE_CASE				).replace(__SCREAMING_SNAKE_CASE     ,    " "				).strip()
            return out_string







       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase = None				)      ->  Optional[int]:
            if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				os.path.join(
                __SCREAMING_SNAKE_CASE     ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

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

            return (out_vocab_file,)
def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


if __name__ == "__main__":
  print(f'''{solution() = }''')


