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

import sentencepiece as spm

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


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{'''vocab_file''': '''sentencepiece.model'''}

UpperCAmelCase       =					{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

UpperCAmelCase       =					{
    '''google/rembert''': 256,
}







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[Any]									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	Union[str, Any]									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	List[str]									=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(			self						,       snake_case						,       snake_case=False						,       snake_case=True						,       snake_case=True						,       snake_case="[CLS]"						,       snake_case="[SEP]"						,       snake_case="[UNK]"						,       snake_case="[SEP]"						,       snake_case="[PAD]"						,       snake_case="[CLS]"						,       snake_case="[MASK]"						,       **snake_case						,       ):
		super().__init__(
		    do_lower_case=snake_case						,       remove_space=snake_case						,       keep_accents=snake_case						,       bos_token=snake_case						,       eos_token=snake_case						,       unk_token=snake_case						,       sep_token=snake_case						,       pad_token=snake_case						,       cls_token=snake_case						,       mask_token=snake_case						,       **snake_case						,       )

		lowercase	    =       do_lower_case
		lowercase	    =       remove_space
		lowercase	    =       keep_accents
		lowercase	    =       vocab_file

		lowercase	    =       spm.SentencePieceProcessor()
		self.sp_model.Load(snake_case			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return len(self.sp_model			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       {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			):
		lowercase	    =       self.__dict__.copy()
		lowercase	    =       None
		return state





	def __setstate__(			self						,       snake_case			):
		lowercase	    =       d
		lowercase	    =       spm.SentencePieceProcessor()
		self.sp_model.Load(self.vocab_file			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case=False			):
		lowercase	    =       self.sp_model.EncodeAsPieces(snake_case			)
		return pieces





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		return self.sp_model.PieceToId(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		return self.sp_model.IdToPiece(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       self.sp_model.decode_pieces(snake_case			)
		return out_string





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       [self.sep_token_id]
		lowercase	    =       [self.cls_token_id]
		if token_ids_a is None:
			return cls + token_ids_a + sep
		return cls + token_ids_a + sep + token_ids_a + sep





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None						,       snake_case = False			):

		if already_has_special_tokens:
			if token_ids_a is not None:
				raise ValueError(
				    'You should not supply a second sequence if the provided sequence of '
				    'ids is already formatted with special tokens for the model.'			)
			return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

		if token_ids_a is not None:
			return [1] + ([0] * len(snake_case			)) + [1] + ([0] * len(snake_case			)) + [1]
		return [1] + ([0] * len(snake_case			)) + [1]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       [self.sep_token_id]
		lowercase	    =       [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			) * [0] + len(token_ids_a + sep			) * [1]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		if not os.path.isdir(snake_case			):
			logger.error('Vocabulary path ({}) should be a directory'.format(snake_case			)			)
			return
		lowercase	    =       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			):
			copyfile(self.vocab_file						,       snake_case			)

		return (out_vocab_file,)


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()