




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				List[Any]:
      return {remove_prefix(__UpperCAmelCase    ,    'model.'							): v for k, v in sd.items()}

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    list(Path(__UpperCAmelCase							).glob('*.ckpt'							)							)
            assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

      if len(__UpperCAmelCase							) > 1:
            logger.info(f"averaging the weights of {ckpt_files}"							)

      SCREAMING_SNAKE_CASE_					=    [sanitize(torch.load(__UpperCAmelCase    ,    map_location='cpu'							)['state_dict']							) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_					=    average_state_dicts(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(__UpperCAmelCase							)
      try:
            SCREAMING_SNAKE_CASE_					=    AutoTokenizer.from_pretrained(__UpperCAmelCase							)
            tok.save_pretrained(__UpperCAmelCase							)
      except Exception:
            pass
            # dont copy tokenizer if cant


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





import bsa
import requests

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


if __name__ == "__main__":
   import json

   lowerCamelCase__				:     int			       =							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)
       )
   )