





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   :							List[Any]     =	torch.cuda.is_available()
_UpperCamelCase   :							str     =	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCamelCase   :							List[str]     =	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCamelCase   :							List[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""": 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-0_8,
    """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     )  ->						Optional[Any]:


   '''simple docstring'''







   __A				 =       '''\n'''.join(snake_case     )
   Path(snake_case     ).open('''w'''     ).writelines(snake_case     )
def 				__UpperCamelCase  (		snake_case     )  ->						Dict:


   '''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 						_lowerCAmelCase(						_a):

  """simple docstring"""







  @classmethod
  def 		SCREAMING_SNAKE_CASE__	( cls     )-> int:
     return cls

  @require_torch_multi_gpu
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       {
         '''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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase=True     )-> Any:
     __A				 =       {
         '''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				 =       CHEAP_ARGS.copy()
     __A				 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
     __A				 =       self.get_auto_remove_tmp_dir()
     args_d.update(data_dir=UpperCAmelCase  ,    output_dir=UpperCAmelCase  ,    **UpperCAmelCase     )

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

     __A				 =       os.listdir(UpperCAmelCase     )
     __A				 =       {os.path.basename(UpperCAmelCase     ) for p in contents}
     __A				 =       [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				 =       os.path.join(UpperCAmelCase  ,    '''metrics.json'''     )
     __A				 =       '''rouge2'''

     __A				 =       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				 =       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     )
     __A				 =       int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1     )
     self.assertEqual(len(metrics['''val''']     )  ,    UpperCAmelCase     )








from __future__ import annotations

import sys
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						tuple[int, int]:


   '''simple docstring'''







   if abs(snake_case     ) == 1:
      return a, 0
   elif abs(snake_case     ) == 1:
      return 0, b

   __A ,						__A				 =       a, b
   __A ,						__A				 =       1, 0
   __A ,						__A				 =       0, 1

   while remainder != 0:
      __A				 =       old_remainder // remainder
      __A ,						__A				 =       remainder, old_remainder - quotient * remainder
      __A ,						__A				 =       coeff_a, old_coeff_a - quotient * coeff_a
      __A ,						__A				 =       coeff_b, old_coeff_b - quotient * coeff_b

   # sign correction for negative numbers
   if a < 0:
      __A				 =       -old_coeff_a
   if b < 0:
      __A				 =       -old_coeff_b

   return old_coeff_a, old_coeff_b
def 				__UpperCamelCase  (		)  ->						Tuple:


   '''simple docstring'''







   if len(sys.argv     ) < 3:
      print('''2 integer arguments required'''     )
      return 1
   __A				 =       int(sys.argv[1]     )
   __A				 =       int(sys.argv[2]     )
   print(extended_euclidean_algorithm(snake_case    ,	snake_case     )     )
   return 0


if __name__ == "__main__":
    raise SystemExit(main())

