import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Optional[int]         =	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE		:     Optional[int]         =	{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE		:     str         =	{
    "RUCAIBox/mvp": 1024,
}

class        UpperCamelCase						(				__a				):
						a__		:str      =			VOCAB_FILES_NAMES
						a__		:Tuple      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Optional[Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[str]      =			['''input_ids''', '''attention_mask''']
						a__		:Optional[Any]      =			MvpTokenizer





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase="replace"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<mask>"			,     __UpperCamelCase=False			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(
												    __UpperCamelCase			,     __UpperCamelCase			,     tokenizer_file=__UpperCamelCase			,     errors=__UpperCamelCase			,     bos_token=__UpperCamelCase			,     eos_token=__UpperCamelCase			,     sep_token=__UpperCamelCase			,     cls_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     pad_token=__UpperCamelCase			,     mask_token=__UpperCamelCase			,     add_prefix_space=__UpperCamelCase			,     trim_offsets=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
												if pre_tok_state.get("""add_prefix_space"""			,     __UpperCamelCase						) != add_prefix_space:
																		UpperCamelCase_		:							Dict  =		getattr(__UpperCamelCase			,     pre_tok_state.pop("""type"""						)						)
																		UpperCamelCase_		:							Tuple  =		add_prefix_space
																		UpperCamelCase_		:							Optional[int]  =		pre_tok_class(**__UpperCamelCase						)

												UpperCamelCase_		:							List[Any]  =		add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												UpperCamelCase_		:							List[str]  =		"""post_processor"""
												UpperCamelCase_		:							Any  =		getattr(self.backend_tokenizer			,     __UpperCamelCase			,     __UpperCamelCase						)
												if tokenizer_component_instance:
																		UpperCamelCase_		:							Any  =		json.loads(tokenizer_component_instance.__getstate__()						)

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																								UpperCamelCase_		:							Optional[Any]  =		tuple(state["""sep"""]						)
																		if "cls" in state:
																								UpperCamelCase_		:							List[Any]  =		tuple(state["""cls"""]						)

																		UpperCamelCase_		:							Union[str, Any]  =		False

																		if state.get("""add_prefix_space"""			,     __UpperCamelCase						) != add_prefix_space:
																								UpperCamelCase_		:							Any  =		add_prefix_space
																								UpperCamelCase_		:							Any  =		True

																		if state.get("""trim_offsets"""			,     __UpperCamelCase						) != trim_offsets:
																								UpperCamelCase_		:							List[Any]  =		trim_offsets
																								UpperCamelCase_		:							Tuple  =		True

																		if changes_to_apply:
																								UpperCamelCase_		:							str  =		getattr(__UpperCamelCase			,     state.pop("""type"""						)						)
																								UpperCamelCase_		:							Optional[Any]  =		component_class(**__UpperCamelCase						)
																								setattr(self.backend_tokenizer			,     __UpperCamelCase			,     __UpperCamelCase						)





						@property
						def        A_							(self						)				->				str:
												if self._mask_token is None:
																		if self.verbose:
																								logger.error("""Using mask_token, but it is not set yet."""						)
																		return None
												return str(self._mask_token						)





						@mask_token.setter
						def        A_							(self			,     __UpperCamelCase						)				->				Tuple:
												UpperCamelCase_		:							Dict  =		AddedToken(__UpperCamelCase			,     lstrip=__UpperCamelCase			,     rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase			,     __UpperCamelCase						) else value
												UpperCamelCase_		:							str  =		value





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				BatchEncoding:
												UpperCamelCase_		:							Tuple  =		kwargs.get("""is_split_into_words"""			,     __UpperCamelCase						)

												if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																		    """to use it with pretokenized inputs."""						)

												return super()._batch_encode_plus(*__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				BatchEncoding:
												UpperCamelCase_		:							Union[str, Any]  =		kwargs.get("""is_split_into_words"""			,     __UpperCamelCase						)

												if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																		    """to use it with pretokenized inputs."""						)

												return super()._encode_plus(*__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												UpperCamelCase_		:							Any  =		self._tokenizer.model.save(__UpperCamelCase			,     name=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=None						)				->				str:
												UpperCamelCase_		:							Optional[Any]  =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				List[int]:
												UpperCamelCase_		:							Optional[Any]  =		[self.sep_token_id]
												UpperCamelCase_		:							Optional[int]  =		[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]







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





						def __getstate__(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.__dict__.copy()
												UpperCamelCase_		:							List[str]  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

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

												UpperCamelCase_		:							Dict  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, Any]  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__UpperCamelCase						) + token
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		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:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






