



'''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


UpperCamelCase__    :     Any			  =   torch.cuda.is_available()
UpperCamelCase__    :     Any			  =   [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
UpperCamelCase__    :     List[Any]			  =   ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
UpperCamelCase__    :     Any			  =   {
    """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     UpperCAmelCase     (    a_      ,		a_		)   ->       str:



   """simple docstring"""






   A_  :		List[str]						=					"""\n""".join(a_		)
   Path(a_		).open("""w"""		).writelines(a_		)



def     UpperCAmelCase     (    a_		)   ->       Any:



   """simple docstring"""






   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 							_lowerCAmelCase	(  A_							):


 """simple docstring"""

 @classmethod
 def       UpperCAmelCase_       (      cls  )    ->    str:
    return cls







 @require_torch_multi_gpu
 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		Optional[Any]						=					{
        """no_teacher""": True,
        """freeze_encoder""": True,
        """gpus""": 2,
        """overwrite_output_dir""": True,
        """sortish_sampler""": True,
    }
    self._test_distiller_cli_fork(__lowercase					,    check_contents=__lowercase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=True  )    ->    Optional[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(__lowercase  )
    A_  :		Union[str, Any]						=					CHEAP_ARGS.copy()
    A_  :		Any						=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
    A_  :		List[Any]						=					self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=__lowercase					,    output_dir=__lowercase					,    **__lowercase  )

    def convert(_lowerCamelCase					,    _lowerCamelCase  ):
       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_  :		List[str]						=					[x for x in (convert(__lowercase					,    __lowercase  ) for k, v in args_d.items()) if len(__lowercase  )]
    A_  :		Optional[Any]						=					[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
    execute_subprocess_async(__lowercase					,    env=self.get_env()  )

    A_  :		List[Any]						=					os.listdir(__lowercase  )
    A_  :		List[str]						=					{os.path.basename(__lowercase  ) for p in contents}
    A_  :		List[str]						=					[p for p in contents if p.endswith("""ckpt"""  )]
    assert len(__lowercase  ) > 0

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

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

    A_  :		Union[str, Any]						=					load_json(__lowercase  )
    # {'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(__lowercase  )
    A_  :		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}"]					,    __lowercase  )
    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"""]  )					,    __lowercase  )







def  A_(      A ):
	if not isinstance(A					,   A ):
		UpperCAmelCase_		    =  f"""Input value of [number={number}] must be an integer"""
		raise TypeError(A )
	if number < 0 or number % 2 == 1:
		return False
	UpperCAmelCase_		    =  int(number**0.5 )
	return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()


