




import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	Any:



       """simple docstring"""

       A__							=   FunnelConfig.from_json_file(lowercase_						)
       print(f"""Building PyTorch model from configuration: {config}"""						)
       A__							=   FunnelBaseModel(lowercase_						) if base_model else FunnelModel(lowercase_						)

       # Load weights from tf checkpoint
       load_tf_weights_in_funnel(lowercase_			,    lowercase_			,    lowercase_						)

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
       torch.save(model.state_dict()			,    lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
  )
  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
  )








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()


