




import numpy as np

from transformers import Pipeline







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[Any]:



       """simple docstring"""

       A__							=   np.max(lowerCAmelCase__			,    axis=-1			,    keepdims=lowerCAmelCase__						)
       A__							=   np.exp(outputs - maxes						)
       return shifted_exp / shifted_exp.sum(axis=-1			,    keepdims=lowerCAmelCase__						)


class     UpperCamelCase_   (			__lowercase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			**UpperCAmelCase__				:     List[str])						->Any:





           '''simple docstring'''

           A__							=   {}
           if "second_text" in kwargs:
                  A__							=   kwargs['''second_text''']
           return preprocess_kwargs, {}, {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Dict=None)						->Optional[Any]:





           '''simple docstring'''

           return self.tokenizer(snake_case_ ,			text_pair=snake_case_ ,			return_tensors=self.framework)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[Any]:





           '''simple docstring'''

           return self.model(**snake_case_)





    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Dict)						->List[Any]:





           '''simple docstring'''

           A__							=   model_outputs.logits[0].numpy()
           A__							=   softmax(snake_case_)

           A__							=   np.argmax(snake_case_)
           A__							=   self.model.config.idalabel[best_class]
           A__							=   probabilities[best_class].item()
           A__							=   logits.tolist()
           return {"label": label, "score": score, "logits": logits}










from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""						)       ->	dict[str, float]:



       """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 SCREAMING_SNAKE_CASE    (	lowercase_ = "IMDb_Top_250_Movies.csv"						)       ->	None:



       """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()


