
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				:Optional[Any]									=							logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE				:Optional[int]									=							{'vocab_file': 'spiece.model'}

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

SCREAMING_SNAKE_CASE				:Optional[int]									=							{
    'google/reformer-crime-and-punishment': 52_4288,
}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''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   :					Tuple				,A   :					Any				,A   :					Any="</s>"				,A   :					Tuple="<unk>"				,A   :					Any=[]				,A   :					Optional[Dict[str, Any]] = None				,**A   :					List[Any]				,):
					__A       =					{} if sp_model_kwargs is None else sp_model_kwargs

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

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






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return self.sp_model.get_piece_size()






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					{self.convert_ids_to_tokens(A					): i for i in range(self.vocab_size					)}
					vocab.update(self.added_tokens_encoder					)
					return vocab






	def __getstate__(			self   :					List[Any]					):
					__A       =					self.__dict__.copy()
					__A       =					None
					return state






	def __setstate__(			self   :					str				,A   :					Any					):
					__A       =					d

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

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






	def 							UpperCamelCase_    (			self   :					int				,A   :					str					):
					return self.sp_model.encode(A				,out_type=A					)






	def 							UpperCamelCase_    (			self   :					str				,A   :					Tuple					):
					return self.sp_model.piece_to_id(A					)






	def 							UpperCamelCase_    (			self   :					str				,A   :					Optional[Any]					):
					if index < self.sp_model.get_piece_size():
									__A       =					self.sp_model.IdToPiece(A					)
					return token






	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					List[str]					):
					__A       =					[]
					__A       =					""
					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(A					) + token
													__A       =					[]
									else:
													current_sub_tokens.append(A					)
					out_string += self.sp_model.decode(A					)
					return out_string.strip()







	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					str				,A   :					Optional[str] = None					):
					if not os.path.isdir(A					):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
									return
					__A       =					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:
													__A       =					self.sp_model.serialized_model_proto()
													fi.write(A					)

					return (out_vocab_file,)








"""simple docstring"""







import requests
from bsa import BeautifulSoup


def      UpperCamelCase       ( _A = "AAPL" )     ->       str:
				lowercase    :      Optional[Any]       = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
				lowercase    :      str       = BeautifulSoup(requests.get(_A ).text  ,				"""html.parser""" )
				lowercase    :      int       = """My(6px) Pos(r) smartphone_Mt(6px)"""
				return soup.find("""div"""  ,				class_=class_ ).find("""span""" ).text


if __name__ == "__main__":
				for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
								print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
