

import bsa
import requests

def      a__	(						UpperCAmelCase				:     bsa.element.ResultSet     )    ->		int:
  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      a__	(						UpperCAmelCase				:     int = 5     )    ->		Optional[Any]:
  UpperCAmelCase			:				str										=					int(float(UpperCAmelCase     )     )
  if num_movies < 1:
    return ()
  UpperCAmelCase			:				List[str]										=					(
      '''https://www.imdb.com/search/title?title_type='''
      f'''feature&sort=num_votes,desc&count={num_movies}'''
  )
  UpperCAmelCase			:				Optional[int]										=					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							:  Dict     =				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)
         )
     )











"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

					trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
					__snake_case        =						gpt.pos_emb
					trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()		)
					trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()		)
					trajectory_transformer.head.load_state_dict(gpt.head.state_dict()		)

					for i, block in enumerate(gpt.blocks		):
										trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
										trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
										trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()		)

										trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()		)
										trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()		)
										trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()		)
										trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()		)

					torch.save(trajectory_transformer.state_dict()					,       """pytorch_model.bin"""		)


if __name__ == "__main__":
		UpperCamelCase__			:     int			       =				Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)


