



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


lowercase_	       =			logging.get_logger(__name__)


lowercase_	       =			'''▁'''

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

lowercase_	       =			{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase_	       =			{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE       =       PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE       =       ["input_ids", "attention_mask"]



	def __init__(						self							:    str				,	snake_case_							:    str				,	snake_case_							:    int="</s>"				,	snake_case_							:    Optional[Any]="<unk>"				,	snake_case_							:    str=[]				,	snake_case_							:    Optional[Dict[str, Any]] = None				,	**snake_case_							:    Dict				,	)->							None:
						__lowerCAmelCase	   ={} if sp_model_kwargs is None else sp_model_kwargs

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

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



	@property
	def 		UpperCamelCase							(						self							:    str)->							int:
						return self.sp_model.get_piece_size()



	def 		UpperCamelCase							(						self							:    List[str])->							Dict[str, int]:
						__lowerCAmelCase	   ={self.convert_ids_to_tokens(snake_case_): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab



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



	def __setstate__(						self							:    Dict				,	snake_case_							:    Union[str, Any])->							Dict:
						__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 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    str)->							List[str]:
						return self.sp_model.encode(snake_case_				,	out_type=snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    List[Any])->							List[str]:
						return self.sp_model.piece_to_id(snake_case_)



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    int)->							List[Any]:
						if index < self.sp_model.get_piece_size():
											__lowerCAmelCase	   =self.sp_model.IdToPiece(snake_case_)
						return token



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    Optional[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =""""""
						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(snake_case_) + token
																__lowerCAmelCase	   =[]
											else:
																current_sub_tokens.append(snake_case_)
						out_string += self.sp_model.decode(snake_case_)
						return out_string.strip()



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    str				,	snake_case_							:    Optional[str] = None)->							Tuple[str]:
						if not os.path.isdir(snake_case_):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
											return
						__lowerCAmelCase	   =os.path.join(
						    snake_case_				,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

						return (out_vocab_file,)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




