




"""simple docstring"""




from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







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







def      _a				(						SCREAMING_SNAKE_CASE_							:    str = "IMDb_Top_250_Movies.csv"					):
					__lowerCAmelCase           =	get_imdb_top_aaa_movies()
					with open(SCREAMING_SNAKE_CASE_				,				"w"				,				newline=""					) as out_file:
										__lowerCAmelCase           =	csv.writer(SCREAMING_SNAKE_CASE_					)
										writer.writerow(["Movie title", "IMDb rating"]					)
										for title, rating in movies.items():
															writer.writerow([title, rating]					)


if __name__ == "__main__":
					write_movies()










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					for split in ds.keys():
										print(F"""Splitting {split} with {ds[split].num_rows} records"""					)

										# to save to val.source, val.target like summary datasets
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	tgt_path.open("w+"					)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]					):
															__lowerCAmelCase           =	x["translation"]
															src_fp.write(ex[src_lang] + "\n"					)
															tgt_fp.write(ex[tgt_lang] + "\n"					)

					print(F"""Saved {dataset} dataset to {save_dir}"""					)


if __name__ == "__main__":
					fire.Fire(download_wmt_dataset)




