






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


_snake_case					   =     torch.cuda.is_available()
_snake_case					   =     [' Sam ate lunch today.', 'Sams lunch ingredients.']
_snake_case					   =     ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_snake_case					   =     {
    '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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: int    =			"""\n""".join(UpperCamelCase__       )
  Path(UpperCamelCase__       ).open("""w"""       ).writelines(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(UpperCamelCase__  ,     F"""{split}.source"""       )  ,     UpperCamelCase__       )
    _dump_articles(os.path.join(UpperCamelCase__  ,     F"""{split}.target"""       )  ,     UpperCamelCase__       )
  return tmp_dir






class  UpperCamelCase    (			snake_case_   ):


 @classmethod
 def _lowercase			(  cls	:		List[str]							)      -> str:
   return cls


 @require_torch_multi_gpu
 def _lowercase			(  self	:		Optional[Any]							)      -> Optional[int]:
   _a						: Dict    =			{
       """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 _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Any							,		UpperCAmelCase__	:		int=True							)      -> Any:
   _a						: Any    =			{
       """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__							)
   _a						: dict    =			CHEAP_ARGS.copy()
   _a						: str    =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
   _a						: List[str]    =			self.get_auto_remove_tmp_dir()
   args_d.update(data_dir=UpperCAmelCase__							,		output_dir=UpperCAmelCase__							,		**UpperCAmelCase__							)

   def convert(UpperCAmelCase__	:		List[str]							,		UpperCAmelCase__	:		Tuple							):
     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}"""

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

   _a						: List[str]    =			os.listdir(UpperCAmelCase__							)
   _a						: Tuple    =			{os.path.basename(UpperCAmelCase__							) for p in contents}
   _a						: 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)
   _a						: Any    =			os.path.join(UpperCAmelCase__							,		"""metrics.json"""							)
   _a						: List[Any]    =			"""rouge2"""

   _a						: Union[str, 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__							)
   _a						: Optional[int]    =			metrics["""val"""][-1]
   self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]							,		0.0_1							)
   self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]							,		UpperCAmelCase__							)
   self.assertEqual(len(metrics["""test"""]							)							,		1							)
   _a						: Tuple    =			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1							)
   self.assertEqual(len(metrics["""val"""]							)							,		UpperCAmelCase__							)









"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5  ,     int(math.sqrt(UpperCamelCase__       ) + 1       )  ,     6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def     lowerCAmelCase__  (  UpperCamelCase__ = 7       ):



  '''simple docstring'''
  _a						: Tuple    =			"""""".join(str(UpperCamelCase__       ) for i in range(1  ,     n + 1       )       )
  _a						: str    =			[int("""""".join(UpperCamelCase__       )       ) for i in permutations(UpperCamelCase__  ,     UpperCamelCase__       )]
  _a						: Union[str, Any]    =			[num for num in perm_list if is_prime(UpperCamelCase__       )]
  return max(UpperCamelCase__       ) if pandigitals else 0


if __name__ == "__main__":
 print(F'''{solution() = }''')

