






import torch

from transformers import AutoModel



class 				_UpperCamelCase      (torch.nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase="sayef/fsner-bert-base-uncased"						)->						str:
           super(__UpperCamelCase       ,    self						).__init__()

           __lowerCAmelCase             =       AutoModel.from_pretrained(__UpperCamelCase       ,    return_dict=__UpperCamelCase						)
           __lowerCAmelCase             =       torch.nn.CosineSimilarity(3       ,    1e-08						)
           __lowerCAmelCase             =       torch.nn.Softmax(dim=1						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Dict:
           return self.bert(**__UpperCamelCase						).last_hidden_state


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Union[str, Any]:
           return token_embeddings.sum(2       ,    keepdim=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=1						)->						Dict:
           return self.softmax(T * self.cos(__UpperCamelCase       ,    __UpperCamelCase						)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       W_supports["sizes"].tolist()
           __lowerCAmelCase             =       W_supports["start_token_id"].item()
           __lowerCAmelCase             =       W_supports["end_token_id"].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           __lowerCAmelCase             =       self.BERT(**__UpperCamelCase						)
           __lowerCAmelCase             =       self.BERT(**__UpperCamelCase						)

           __lowerCAmelCase             =       None
           __lowerCAmelCase             =       None

           __lowerCAmelCase             =       W_supports["input_ids"] == start_token_id
           __lowerCAmelCase             =       W_supports["input_ids"] == end_token_id

           for i, size in enumerate(__UpperCamelCase						):
                if i == 0:
                     __lowerCAmelCase             =       0
                else:
                     __lowerCAmelCase             =       support_sizes[i - 1]

                __lowerCAmelCase             =       S[s : s + size][start_token_masks[s : s + size]]
                __lowerCAmelCase             =       S[s : s + size][end_token_masks[s : s + size]]

                __lowerCAmelCase             =       torch.matmul(q[i]       ,    s_start.T						).sum(1						).softmax(0						)
                __lowerCAmelCase             =       torch.matmul(q[i]       ,    s_end.T						).sum(1						).softmax(0						)

                if p_starts is not None:
                     __lowerCAmelCase             =       torch.vstack((p_starts, p_start)						)
                     __lowerCAmelCase             =       torch.vstack((p_ends, p_end)						)
                else:
                     __lowerCAmelCase             =       p_start
                     __lowerCAmelCase             =       p_end

           return p_starts, p_ends














from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def    __lowerCAmelCase   (    __snake_case = ""							):
     __lowerCAmelCase             =       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     __lowerCAmelCase             =       BeautifulSoup(requests.get(__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(__snake_case       ,			__snake_case							)
     }





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


if __name__ == "__main__":
 write_movies()






