






"""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 :    List[Any]      =			torch.cuda.is_available()
_UpperCamelCase :    Dict      =			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCamelCase :    Union[str, Any]      =			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCamelCase :    List[str]      =			{
    """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			(  UpperCamelCase_				:					Path  ,						UpperCamelCase_				:					list		)	->		str:
 __A						:							List[Any]      =						'\n'.join(UpperCamelCase_		)
 Path(UpperCamelCase_		).open('w'		).writelines(UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					Union[str, Any]		)	->		Union[str, Any]:
 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 __a ( snake_case_					):



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



  @require_torch_multi_gpu
  def     A_					(self	:		List[str] )		->				Optional[Any]:
   __A						:							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     A_					(self	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int=True )		->				List[str]:
   __A						:							Optional[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(lowercase_ )
   __A						:							dict      =						CHEAP_ARGS.copy()
   __A						:							int      =						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(lowercase_	:		str					,      lowercase_	:		Dict ):
    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						:							Optional[Any]      =						[x for x in (convert(lowercase_					,      lowercase_ ) for k, v in args_d.items()) if len(lowercase_ )]
   __A						:							Any      =						[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
   execute_subprocess_async(lowercase_					,      env=self.get_env() )

   __A						:							Tuple      =						os.listdir(lowercase_ )
   __A						:							Dict      =						{os.path.basename(lowercase_ ) for p in contents}
   __A						:							List[Any]      =						[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						:							Optional[int]      =						'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						:							Any      =						int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1 )
   self.assertEqual(len(metrics['val'] )					,      lowercase_ )













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





