

'''simple docstring'''

import bsa
import requests

def      __magic_name__							(			A				)      ->		dict[str, str]:
	return {
	    "name": soup.ha.a.text,
	    "genre": soup.find('span'       , class_='genre'				).text.strip(),
	    "rating": soup.strong.text,
	    "page_link": F'''https://www.imdb.com{soup.a.get("href"				)}''',
	}

def      __magic_name__							(			A = 5				)      ->		tuple:
	snake_case							=					int(float(A				)				)
	if num_movies < 1:
		return ()
	snake_case							=					(
	    'https://www.imdb.com/search/title?title_type='
	    F'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	snake_case							=					bsa.BeautifulSoup(requests.get(A				).content       , 'html.parser'				)
	return tuple(
	    get_movie_data_from_soup(A				)
	    for movie in source.find_all('div'       , class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
						import json

						lowerCAmelCase_     =      int(input("How many movies would you like to see? "))
						print(
						    ", ".join(
						        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
						    )
						)








from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    task_template_dict.get('task'							)
    if task_name is None:
        logger.warning(f"""Couldn't find template for task '{task_name}'. Available templates: {list(_SCREAMING_SNAKE_CASE							)}"""							)
        return None
    SCREAMING_SNAKE_CASE_						=    NAMEaTEMPLATE.get(_SCREAMING_SNAKE_CASE							)
    return template.from_dict(_SCREAMING_SNAKE_CASE							)


