
"""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            = torch.cuda.is_available()
_A            = [' Sam ate lunch today.', 'Sams lunch ingredients.']
_A            = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_A            = {
    '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 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    "\n".join(__lowerCamelCase			)
		Path(__lowerCamelCase			).open('w'			).writelines(__lowerCamelCase			)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


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






class        _lowercase   (				__lowercase		):




	@classmethod
	def 			_UpperCamelCase    ( cls					)		->							Optional[int]:
			return cls




	@require_torch_multi_gpu
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			str		=    {
			    "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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=True					)		->							List[str]:
			lowerCamelCase     :			Dict		=    {
			    "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					)
			lowerCamelCase     :			dict		=    CHEAP_ARGS.copy()
			lowerCamelCase     :			Optional[Any]		=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
			lowerCamelCase     :			Union[str, Any]		=    self.get_auto_remove_tmp_dir()
			args_d.update(data_dir=__lowerCamelCase		, output_dir=__lowerCamelCase		, **__lowerCamelCase					)

			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}"""

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

			lowerCamelCase     :			Optional[int]		=    os.listdir(__lowerCamelCase					)
			lowerCamelCase     :			List[str]		=    {os.path.basename(__lowerCamelCase					) for p in contents}
			lowerCamelCase     :			str		=    [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)
			lowerCamelCase     :			List[str]		=    os.path.join(__lowerCamelCase		, 'metrics.json'					)
			lowerCamelCase     :			Union[str, Any]		=    "rouge2"

			lowerCamelCase     :			List[str]		=    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					)
			lowerCamelCase     :			List[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}"""]		, __lowerCamelCase					)
			self.assertEqual(len(metrics['test']					)		, 1					)
			lowerCamelCase     :			Dict		=    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1					)
			self.assertEqual(len(metrics['val']					)		, __lowerCamelCase					)













import sys

sys.set_int_max_str_digits(0)  # type: ignore






def 			lowerCamelCase__	(__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		int  			=							[0] * 11
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[0] * 11

     # mark last 9 numbers
     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1
          _SCREAMING_SNAKE_CASE				:		str  			=							number // 10
     # flag
     _SCREAMING_SNAKE_CASE				:		int  			=							True

     # check last 9 numbers for pandigitality

     for x in range(9		):
          if not check_last[x + 1]:
               _SCREAMING_SNAKE_CASE				:		Any  			=							False
     if not f:
          return f

     # mark first 9 numbers
     _SCREAMING_SNAKE_CASE				:		str  			=							int(str(__lowerCamelCase		)[:9]		)

     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		str  			=							1
          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							number // 10

     # check first 9 numbers for pandigitality

     for x in range(9		):
          if not check_front[x + 1]:
               _SCREAMING_SNAKE_CASE				:		Any  			=							False
     return f






def 			lowerCamelCase__	(__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[0] * 11

     # mark last 9 numbers
     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		Dict  			=							1
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							number // 10
     # flag
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							True

     # check last 9 numbers for pandigitality

     for x in range(9		):
          if not check_last[x + 1]:
               _SCREAMING_SNAKE_CASE				:		str  			=							False
     return f






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							1
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							1
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							2
     # temporary Fibonacci numbers

     _SCREAMING_SNAKE_CASE				:		Any  			=							1
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							1
     _SCREAMING_SNAKE_CASE				:		str  			=							2
     # temporary Fibonacci numbers mod 1e9

     # mod m=1e9, done for fast optimisation
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[0] * 1000000
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							1000000000

     for x in range(1000000		):
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(aa + ba) % m
          _SCREAMING_SNAKE_CASE				:		int  			=							ba % m
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							ca % m
          if checka(__lowerCamelCase		):
               _SCREAMING_SNAKE_CASE				:		List[Any]  			=							1

     for x in range(1000000		):
          _SCREAMING_SNAKE_CASE				:		Any  			=							a + b
          _SCREAMING_SNAKE_CASE				:		Dict  			=							b
          _SCREAMING_SNAKE_CASE				:		int  			=							c
          # perform check only if in tocheck
          if tocheck[x + 3] and check(__lowerCamelCase		):
               return x + 3  # first 2 already done
     return -1


if __name__ == "__main__":
       print(f"{solution() = }")