



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     				=						'▁'

SCREAMING_SNAKE_CASE     				=						{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE     				=						{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

SCREAMING_SNAKE_CASE     				=						{
    'google/pegasus-xsum': 512,
}


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)



class 	lowerCamelCase      (						UpperCamelCase_  ):







     '''simple docstring'''







     lowerCAmelCase_				:    Dict									=  VOCAB_FILES_NAMES

     lowerCAmelCase_				:    Dict									=  VOCAB_FILES_NAMES
     lowerCAmelCase_				:    Dict									=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_				:    Optional[int]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_				:    Any									=  ['input_ids', 'attention_mask']



     def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase="<pad>"    ,	lowerCAmelCase="</s>"    ,	lowerCAmelCase="<unk>"    ,	lowerCAmelCase="<mask_2>"    ,	lowerCAmelCase="<mask_1>"    ,	lowerCAmelCase=None    ,	lowerCAmelCase=103    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
       UpperCAmelCase_						=				offset
       if additional_special_tokens is not None:
         if not isinstance(lowerCAmelCase    ,	lowerCAmelCase     ):
           raise TypeError(
               f'''additional_special_tokens should be of type {type(lowerCAmelCase     )}, but is'''
               f''' {type(lowerCAmelCase     )}'''     )

         UpperCAmelCase_						=				(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'''<unk_{i}>''' for i in range(len(lowerCAmelCase     )    ,	self.offset - 1     )
         ]

         if len(set(lowerCAmelCase     )     ) != len(lowerCAmelCase     ):
           raise ValueError(
               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
               f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
         UpperCAmelCase_						=				additional_special_tokens_extended
       else:
         UpperCAmelCase_						=				[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2    ,	self.offset     )]

       UpperCAmelCase_						=				{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	mask_token_sent=lowerCAmelCase    ,	offset=lowerCAmelCase    ,	additional_special_tokens=lowerCAmelCase    ,	sp_model_kwargs=self.sp_model_kwargs    ,	**lowerCAmelCase    ,	)
       UpperCAmelCase_						=				mask_token_sent
       UpperCAmelCase_						=				vocab_file
       UpperCAmelCase_						=				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
       self.sp_model.Load(lowerCAmelCase     )

       # add special tokens to encoder dict
       UpperCAmelCase_						=				{
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
         self.encoder.update(
             {
                 2: self.mask_token_sent,
                 3: self.mask_token,
             }     )

       if self.offset > 0:
         # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
         # mask_token_sent is already added to list -> so start at 1
         self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1    ,	self.offset - 1     )}     )

       UpperCAmelCase_						=				{v: k for k, v in self.encoder.items()}



     @property
     def    A__		(			self     ):
       return len(self.sp_model     ) + self.offset



     def    A__		(			self     ):
       UpperCAmelCase_						=				{self.convert_ids_to_tokens(lowerCAmelCase     ): i for i in range(self.vocab_size     )}
       vocab.update(self.added_tokens_encoder     )
       return vocab



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



     def __setstate__(			self    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				d

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

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



     def    A__		(			self    ,	lowerCAmelCase     ):
       return self.sp_model.encode(lowerCAmelCase    ,	out_type=lowerCAmelCase     )



     def    A__		(			self    ,	lowerCAmelCase     ):
       if token in self.decoder:
         return self.decoder[token]
       elif token in self.added_tokens_decoder:
         return self.added_tokens_decoder[token]
       UpperCAmelCase_						=				self.sp_model.piece_to_id(lowerCAmelCase     )
       return sp_id + self.offset



     def    A__		(			self    ,	lowerCAmelCase     ):
       if index in self.encoder:
         return self.encoder[index]
       elif index in self.added_tokens_encoder:
         return self.added_tokens_encoder[index]
       else:
         UpperCAmelCase_						=				self.sp_model.IdToPiece(index - self.offset     )
       return token



     def    A__		(			self    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				[]
       UpperCAmelCase_						=				''''''
       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(lowerCAmelCase     ) + token
           UpperCAmelCase_						=				[]
         else:
           current_sub_tokens.append(lowerCAmelCase     )
       out_string += self.sp_model.decode(lowerCAmelCase     )
       return out_string.strip()



     def    A__		(			self    ,	lowerCAmelCase=False     ):
       return 1



     def    A__		(			self    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				set(self.all_special_ids     )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

       return [1 if x in all_special_ids else 0 for x in seq]



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = False     ):
       if already_has_special_tokens:
         return self._special_token_mask(lowerCAmelCase     )
       elif token_ids_a is None:
         return self._special_token_mask(lowerCAmelCase     ) + [1]
       else:
         return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
       if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
       if not os.path.isdir(lowerCAmelCase     ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
         return
       UpperCAmelCase_						=				os.path.join(
           lowerCAmelCase    ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

       return (out_vocab_file,)






def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						res += a

				a += a
				b >>= 1

		return res







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						UpperCAmelCase_						=				((res % c) + (a % c)) % c

				a += a
				b >>= 1

		return res

