






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


a    =       torch.cuda.is_available()
a    =       [' Sam ate lunch today.', 'Sams lunch ingredients.']
a    =       ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
a    =       {
    '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': 42,
    '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': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    '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       a__						(  A		,				A					):
   lowercase__						:	Any         = """\n""".join(A					)
   Path(A					).open("""w"""					).writelines(A					)
def       a__						(  A					):
   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(A		,				F'''{split}.source'''					)		,				A					)
      _dump_articles(os.path.join(A		,				F'''{split}.target'''					)		,				A					)
   return tmp_dir






class        __snake_case(					__UpperCamelCase      ):



 @classmethod
 def 						UpperCAmelCase    (	cls   :   Optional[Any]	):

    """simple docstring"""






    return cls



 @require_torch_multi_gpu
 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Any         = {
        """no_teacher""": True,
        """freeze_encoder""": True,
        """gpus""": 2,
        """overwrite_output_dir""": True,
        """sortish_sampler""": True,
    }
    self._test_distiller_cli_fork(UpperCAmelCase__ ,		check_contents=UpperCAmelCase__	)







 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   int=True	):

    """simple docstring"""






    lowercase__						:	int         = {
        """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(UpperCAmelCase__	)
    lowercase__						:	dict         = CHEAP_ARGS.copy()
    lowercase__						:	str         = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
    lowercase__						:	List[Any]         = self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=UpperCAmelCase__ ,		output_dir=UpperCAmelCase__ ,		**UpperCAmelCase__	)

    def convert(UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   Optional[Any]	):
       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}'''

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

    lowercase__						:	Optional[int]         = os.listdir(UpperCAmelCase__	)
    lowercase__						:	Dict         = {os.path.basename(UpperCAmelCase__	) for p in contents}
    lowercase__						:	List[str]         = [p for p in contents if p.endswith("""ckpt"""	)]
    assert len(UpperCAmelCase__	) > 0

    self.assertIn("""test_generations.txt""" ,		UpperCAmelCase__	)
    self.assertIn("""test_results.txt""" ,		UpperCAmelCase__	)

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

    lowercase__						:	List[Any]         = load_json(UpperCAmelCase__	)
    # {'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(UpperCAmelCase__	)
    lowercase__						:	Any         = metrics["""val"""][-1]
    self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""] ,		0.01	)
    self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}'''] ,		UpperCAmelCase__	)
    self.assertEqual(len(metrics["""test"""]	) ,		1	)
    lowercase__						:	Dict         = int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1	)
    self.assertEqual(len(metrics["""val"""]	) ,		UpperCAmelCase__	)










a    =       10**8
def       a__						(  A					):
   if n % 10 == 0:
      return False
   lowercase__						:	Optional[Any]         = str(A					)
   return s == s[::-1]
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = set()
   lowercase__						:	Dict         = 1
   lowercase__						:	List[str]         = 5
   while sum_squares < LIMIT:
      lowercase__						:	Tuple         = first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(A					):
            answer.add(A					)
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      lowercase__						:	Any         = first_square**2 + (first_square + 1) ** 2

   return sum(A					)


if __name__ == "__main__":
 print(solution())


