






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__A      =				logging.get_logger(__name__)

__A      =				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__A      =				{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

__A      =				{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


__A      =				{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					VOCAB_FILES_NAMES
							A_						  =					PRETRAINED_VOCAB_FILES_MAP
							A_						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_						  =					PRETRAINED_INIT_CONFIGURATION
							A_						  =					RoFormerTokenizer

							def __init__(  self:   Any		,     __A:   Union[str, Any]=None		,     __A:   Dict=None		,     __A:   Dict=True		,     __A:   Union[str, Any]="[UNK]"		,     __A:   List[Any]="[SEP]"		,     __A:   List[str]="[PAD]"		,     __A:   List[str]="[CLS]"		,     __A:   List[str]="[MASK]"		,     __A:   int=True		,     __A:   List[Any]=None		,     **__A:   int		,     )    ->				int:
												super().__init__(
												    __A		,     tokenizer_file=__A		,     do_lower_case=__A		,     unk_token=__A		,     sep_token=__A		,     pad_token=__A		,     cls_token=__A		,     mask_token=__A		,     tokenize_chinese_chars=__A		,     strip_accents=__A		,     **__A		,     )

												_A								= json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
												if (
												    pre_tok_state.get('''lowercase'''		,     __A   ) != do_lower_case
												    or pre_tok_state.get('''strip_accents'''		,     __A   ) != strip_accents
												):
																	_A								= getattr(__A		,     pre_tok_state.pop('''type'''   )   )
																	_A								= do_lower_case
																	_A								= strip_accents
																	_A								= pre_tok_class(**__A   )

												_A								= do_lower_case

							def __getstate__(  self:   Union[str, Any]   )    ->				str:
												_A								= self.__dict__.copy()
												_A								= BertPreTokenizer()
												return state

							def __setstate__(  self:   Union[str, Any]		,     __A:   Union[str, Any]   )    ->				Optional[int]:
												_A								= d
												_A								= self.__dict__['''_tokenizer'''].get_vocab()
												_A								= PreTokenizer.custom(JiebaPreTokenizer(__A   )   )

							def       __A (  self:   str		,     __A:   str		,     __A:   str=None   )    ->				int:
												_A								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output

							def       __A (  self:   Tuple		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_A								= [self.sep_token_id]
												_A								= [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       __A (  self:   List[Any]		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												_A								= self._tokenizer.model.save(__A		,     name=__A   )
												return tuple(__A   )



							def       __A (  self:   List[str]		,     __A:   int		,     __A:   List[Any]=None		,     __A:   Union[str, Any]=None		,     __A:   List[str]=False		,     **__A:   Optional[Any]		,     )    ->				Dict:
												_A								= BertPreTokenizer()
												return super().save_pretrained(__A		,     __A		,     __A		,     __A		,     **__A   )











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



