






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 						SCREAMING_SNAKE_CASE				( tf.keras.layers.Layer					):





							"""simple docstring"""

							def __init__(  self:   List[Any]		,     __A:   Dict[str, int]		,     __A:   List[str]		,     __A:   int = None		,     __A:   int = None   )    ->				Any:
												super().__init__()
												_A								= pad_token_id
												_A								= max_length
												_A								= vocab
												_A								= merges
												_A								= BytePairTokenizer(__A		,     __A		,     sequence_length=__A   )

							@classmethod
							def       __A (  cls:   int		,     __A:   GPTaTokenizer		,     *__A:   Any		,     **__A:   Optional[int]   )    ->				int:
												_A								= [''' '''.join(__A   ) for m in tokenizer.bpe_ranks.keys()]
												_A								= tokenizer.get_vocab()
												return cls(__A		,     __A		,     *__A		,     **__A   )

							@classmethod
							def       __A (  cls:   Optional[int]		,     __A:   Union[str, os.PathLike]		,     *__A:   str		,     **__A:   Tuple   )    ->				Optional[Any]:
												_A								= GPTaTokenizer.from_pretrained(__A		,     *__A		,     **__A   )
												return cls.from_tokenizer(__A		,     *__A		,     **__A   )

							@classmethod
							def       __A (  cls:   int		,     __A:   Tuple   )    ->				Tuple:
												return cls(**__A   )

							def       __A (  self:   str   )    ->				List[Any]:
												return {
												    "vocab": self.vocab,
												    "merges": self.merges,
												    "max_length": self.max_length,
												    "pad_token_id": self.pad_token_id,
												}



							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   int = None   )    ->				Dict:
												_A								= self.tf_tokenizer(__A   )
												_A								= tf.ones_like(__A   )

												if self.pad_token_id is not None:
																	# pad the tokens up to max length
																	_A								= max_length if max_length is not None else self.max_length

																	if max_length is not None:
																						_A							,_A								= pad_model_inputs(
																						    __A		,     max_seq_length=__A		,     pad_value=self.pad_token_id   )

												return {"attention_mask": attention_mask, "input_ids": input_ids}











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()



