


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def __UpperCAmelCase       (		__A = ""			)						->					dict[str, float]:



       '''simple docstring'''





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





def __UpperCAmelCase       (		__A = "IMDb_Top_250_Movies.csv"			)						->					None:



       '''simple docstring'''





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


if __name__ == "__main__":
    write_movies()









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





