




'''simple docstring'''





import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowercase__								=						torch.cuda.is_available()
lowercase__								=						[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase__								=						['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase__								=						{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 4_2,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			'\n'.join(snake_case )
   Path(snake_case ).open('''w''' ).writelines(snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(snake_case						,  f"""{split}.source""" )						,  snake_case )
      _dump_articles(os.path.join(snake_case						,  f"""{split}.target""" )						,  snake_case )
   return tmp_dir







class     lowercase  ( UpperCamelCase_):






      @classmethod
      def  A__(cls       )							-> Optional[int]:
         return cls






      @require_torch_multi_gpu
      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			{
             'no_teacher': True,
             'freeze_encoder': True,
             'gpus': 2,
             'overwrite_output_dir': True,
             'sortish_sampler': True,
         }
         self._test_distiller_cli_fork(__a			,	check_contents=__a       )






      def  A__(self			,	a__			,	a__=True       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			{
             'label_smoothing': 0.0,
             'early_stopping_patience': -1,
             'train_batch_size': 1,
             'eval_batch_size': 2,
             'max_epochs': 2,
             'alpha_mlm': 0.2,
             'alpha_ce': 0.8,
             'do_predict': True,
             'model_name_or_path': 'sshleifer/tinier_bart',
             'teacher': CHEAP_ARGS['model_name_or_path'],
             'val_check_interval': 0.5,
         }
         default_updates.update(__a       )
         SCREAMING_SNAKE_CASE__      :					dict							    =			CHEAP_ARGS.copy()
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.get_auto_remove_tmp_dir()
         args_d.update(data_dir=__a			,	output_dir=__a			,	**__a       )

         def convert(a__			,	a__       ):
            if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
            if v is False or v is None:
               return ""
            if v is True:  # or len(str(v))==0:
               return f"""--{k}"""
            return f"""--{k}={v}"""

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[x for x in (convert(__a			,	__a       ) for k, v in args_d.items()) if len(__a       )]
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
         execute_subprocess_async(__a			,	env=self.get_env()       )

         SCREAMING_SNAKE_CASE__      :					int							    =			os.listdir(__a       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			{os.path.basename(__a       ) for p in contents}
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			[p for p in contents if p.endswith('''ckpt'''       )]
         assert len(__a       ) > 0

         self.assertIn('''test_generations.txt'''			,	__a       )
         self.assertIn('''test_results.txt'''			,	__a       )

         # get the following from the module, (we don't have access to `model` here)
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			os.path.join(__a			,	'''metrics.json'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'rouge2'

         SCREAMING_SNAKE_CASE__      :					Any							    =			load_json(__a       )
         # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
         print(__a       )
         SCREAMING_SNAKE_CASE__      :					str							    =			metrics['val'][-1]
         self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']			,	0.01       )
         self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]			,	__a       )
         self.assertEqual(len(metrics['''test''']       )			,	1       )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
         self.assertEqual(len(metrics['''val''']       )			,	__a       )









'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   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 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= 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)
          )
      )