


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			:					Tuple         =							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			:					Optional[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_	(					__snake_case      :    Path						,      __snake_case      :    list  )->   List[Any]:




						'''simple docstring'''
						__magic_name__					=      '\n'.join(_lowercase  )
						Path(_lowercase  ).open('w'  ).writelines(_lowercase  )
def 				UpperCamelCase_	(					__snake_case      :    Dict  )->   int:




						'''simple docstring'''
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(_lowercase						,      F'''{split}.source'''  )						,      _lowercase  )
												_dump_articles(os.path.join(_lowercase						,      F'''{split}.target'''  )						,      _lowercase  )
						return tmp_dir




class      __snake_case				(   _lowercase):
					@classmethod
					def 	a			(						cls       :						Dict  )							->					Optional[int]:



											'''simple docstring'''





											return cls
					@require_torch_multi_gpu
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      {
											    'no_teacher': True,
											    'freeze_encoder': True,
											    'gpus': 2,
											    'overwrite_output_dir': True,
											    'sortish_sampler': True,
											}
											self._test_distiller_cli_fork(__lowerCAmelCase	,      check_contents=__lowerCAmelCase  )







					def 	a			(						self       :						Optional[int]	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						str=True  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      {
											    '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(__lowerCAmelCase  )
											__magic_name__					=      CHEAP_ARGS.copy()
											__magic_name__					=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
											__magic_name__					=      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=__lowerCAmelCase	,      output_dir=__lowerCAmelCase	,      **__lowerCAmelCase  )

											def convert(_lowerCAmelCase       :						Tuple	,      _lowerCAmelCase       :						Optional[int]  ):
																	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}'''

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

											__magic_name__					=      os.listdir(__lowerCAmelCase  )
											__magic_name__					=      {os.path.basename(__lowerCAmelCase  ) for p in contents}
											__magic_name__					=      [p for p in contents if p.endswith('ckpt'  )]
											assert len(__lowerCAmelCase  ) > 0

											self.assertIn('test_generations.txt'	,      __lowerCAmelCase  )
											self.assertIn('test_results.txt'	,      __lowerCAmelCase  )

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

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








def      a    (   _lowercase :  Dict			,    _lowercase :  List[str]							)  ->List[Any]:
		if len(str(_lowercase							)							) == 1 or len(str(_lowercase							)							) == 1:
				return a * b

		UpperCamelCase__								=						max(len(str(_lowercase							)							)			,    len(str(_lowercase							)							)							)
		UpperCamelCase__								=						ma // 2

		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)
		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)

		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)
		UpperCamelCase__								=						karatsuba((aa + aa)			,    (ba + ba)							)
		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)

		return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)

def      a    (   )  ->List[Any]:
		print(karatsuba(1_5_4_6_3			,    2_3_4_8_9							)							)


if __name__ == "__main__":
				main()





