






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


lowerCAmelCase_     =			torch.cuda.is_available()
lowerCAmelCase_     =			[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCAmelCase_     =			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCAmelCase_     =			{
    '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-0_8,
    '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   snake_case		(				UpperCAmelCase						:							Path,    UpperCAmelCase						:							list ):
				A									=   '\n'.join(UpperCAmelCase )
				Path(UpperCAmelCase ).open('w' ).writelines(UpperCAmelCase )







def   snake_case		(				UpperCAmelCase						:							Optional[int] ):
				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__			):







		"""simple docstring"""



		@classmethod
		def    A(	cls		:							Optional[Any]		)     -> Optional[Any]:


						'''simple docstring'''
						return cls
		@require_torch_multi_gpu
		def    A(	self		:							List[str]		)     -> Tuple:


						'''simple docstring'''
						A									=   {
						    '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    A(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							int=True		)     -> Tuple:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		)
						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=_SCREAMING_SNAKE_CASE    ,output_dir=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)

						def convert(_SCREAMING_SNAKE_CASE		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]		):
										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(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) for k, v in args_d.items()) if len(_SCREAMING_SNAKE_CASE		)]
						A									=   [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
						execute_subprocess_async(_SCREAMING_SNAKE_CASE    ,env=self.get_env()		)

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

						A									=   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		)
						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}']    ,_SCREAMING_SNAKE_CASE		)
						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']		)    ,_SCREAMING_SNAKE_CASE		)













def   snake_case		(				UpperCAmelCase						:							set,    UpperCAmelCase						:							set ):
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_a={set_a}] is not a set'
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_b={set_b}] is not a set'

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()





