





"""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


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

 '''simple docstring'''






 snake_case__      							=			"\n".join(_UpperCAmelCase		)
 Path(_UpperCAmelCase		).open("w"		).writelines(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					Any:

 '''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        lowercase_			(       __lowerCAmelCase							):

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls       ):






        '''simple docstring'''

        return cls

       @require_torch_multi_gpu
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			{
            "no_teacher": True,
            "freeze_encoder": True,
            "gpus": 2,
            "overwrite_output_dir": True,
            "sortish_sampler": True,
        }
        self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE				,   check_contents=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=True       ):






        '''simple docstring'''

        snake_case__      							=			{
            "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(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			CHEAP_ARGS.copy()
        snake_case__      							=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
        snake_case__      							=			self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=__SCREAMING_SNAKE_CASE				,   output_dir=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

        def convert(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):
         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}"

        snake_case__      							=			[x for x in (convert(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE       )]
        snake_case__      							=			[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
        execute_subprocess_async(__SCREAMING_SNAKE_CASE				,   env=self.get_env()       )

        snake_case__      							=			os.listdir(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			{os.path.basename(__SCREAMING_SNAKE_CASE       ) for p in contents}
        snake_case__      							=			[p for p in contents if p.endswith("ckpt"       )]
        assert len(__SCREAMING_SNAKE_CASE       ) > 0

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

        # get the following from the module, (we don't have access to `model` here)
        snake_case__      							=			os.path.join(__SCREAMING_SNAKE_CASE				,   "metrics.json"       )
        snake_case__      							=			"rouge2"

        snake_case__      							=			load_json(__SCREAMING_SNAKE_CASE       )
        # {'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(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			metrics["val"][-1]
        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]				,   0.01       )
        self.assertIsInstance(last_step_stats[F"val_avg_{val_metric}"]				,   __SCREAMING_SNAKE_CASE       )
        self.assertEqual(len(metrics["test"]       )				,   1       )
        snake_case__      							=			int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1       )
        self.assertEqual(len(metrics["val"]       )				,   __SCREAMING_SNAKE_CASE       )








"""simple docstring"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

