


"""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     :   List[Any]			    =			logging.get_logger(__name__)

A     :   Optional[Any]			    =			{"vocab_file": "sentencepiece.bpe.model"}

A     :   Tuple			    =			{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

A     :   Dict			    =			{
    "camembert-base": 5_1_2,
}

A     :   Optional[int]			    =			"▁"
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[Any]				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    Optional[Any]				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Tuple				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    List[Any]				      =["""input_ids""", """attention_mask"""]

							def __init__(     self    ,   __a    ,   __a="<s>"    ,   __a="</s>"    ,   __a="</s>"    ,   __a="<s>"    ,   __a="<unk>"    ,   __a="<pad>"    ,   __a="<mask>"    ,   __a=["<s>NOTUSED", "</s>NOTUSED"]    ,   __a = None    ,   **__a    ,   ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else mask_token

										__lowerCAmelCase				    =			{} 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    ,   additional_special_tokens=__a    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **__a    ,   )
										__lowerCAmelCase				    =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__a	)	)
										__lowerCAmelCase				    =			vocab_file
										# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
										# sentencepiece vocabulary (this is the case for <s> and </s>
										__lowerCAmelCase				    =			{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
										__lowerCAmelCase				    =			len(self.fairseq_tokens_to_ids	)
										__lowerCAmelCase				    =			len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
										__lowerCAmelCase				    =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def 			snake_case				(     self    ,   __a    ,   __a = None	):

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

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = False	):
										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 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.sep_token_id]
										__lowerCAmelCase				    =			[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	):
										return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			{self.convert_ids_to_tokens(__a	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab

							def 			snake_case				(     self    ,   __a	):
										return self.sp_model.encode(__a    ,   out_type=__a	)

							def 			snake_case				(     self    ,   __a	):
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										elif self.sp_model.PieceToId(__a	) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
										return self.fairseq_offset + self.sp_model.PieceToId(__a	)

							def 			snake_case				(     self    ,   __a	):
										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 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			""
										__lowerCAmelCase				    =			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
																__lowerCAmelCase				    =			True
																__lowerCAmelCase				    =			[]
													else:
																current_sub_tokens.append(__a	)
																__lowerCAmelCase				    =			False
										out_string += self.sp_model.decode(__a	)
										return out_string.strip()

							def __getstate__(     self	):
										__lowerCAmelCase				    =			self.__dict__.copy()
										__lowerCAmelCase				    =			None
										return state

							def __setstate__(     self    ,   __a	):
										__lowerCAmelCase				    =			d

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

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



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										if not os.path.isdir(__a	):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
													return
										__lowerCAmelCase				    =			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:
																__lowerCAmelCase				    =			self.sp_model.serialized_model_proto()
																fi.write(__a	)

										return (out_vocab_file,)








"""simple docstring"""


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			1

			while len(_UpperCamelCase      ) < 1e6:
						constant.append(str(_UpperCamelCase      )      )
						i += 1

			__lowerCAmelCase				    =			"".join(_UpperCamelCase      )

			return (
			    int(constant[0]      )
			    * int(constant[9]      )
			    * int(constant[99]      )
			    * int(constant[999]      )
			    * int(constant[9999]      )
			    * int(constant[9_9999]      )
			    * int(constant[99_9999]      )
			)


if __name__ == "__main__":
	print(solution())




