


"""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


__SCREAMING_SNAKE_CASE				:     Any		=     torch.cuda.is_available()
__SCREAMING_SNAKE_CASE				:     Any		=     [' Sam ate lunch today.', 'Sams lunch ingredients.']
__SCREAMING_SNAKE_CASE				:     List[str]		=     ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__SCREAMING_SNAKE_CASE				:     Optional[int]		=     {
    '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					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Tuple:
							snake_case_     							=		"""\n""".join(_SCREAMING_SNAKE_CASE					)
							Path(_SCREAMING_SNAKE_CASE					).open("""w"""					).writelines(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					,			f"""{split}.source"""					)					,			_SCREAMING_SNAKE_CASE					)
														_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					,			f"""{split}.target"""					)					,			_SCREAMING_SNAKE_CASE					)
							return tmp_dir





class   __A  (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



														return cls



							@require_torch_multi_gpu
							def 		lowerCAmelCase							(					self				:   Any							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		{
														    """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 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   Any=True							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		{
														    """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_							)
														snake_case_     							=		CHEAP_ARGS.copy()
														snake_case_     							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
														snake_case_     							=		self.get_auto_remove_tmp_dir()
														args_d.update(data_dir=UpperCAmelCase_		,							output_dir=UpperCAmelCase_		,							**UpperCAmelCase_							)

														def convert(UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[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}"""

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

														snake_case_     							=		os.listdir(UpperCAmelCase_							)
														snake_case_     							=		{os.path.basename(UpperCAmelCase_							) for p in contents}
														snake_case_     							=		[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)
														snake_case_     							=		os.path.join(UpperCAmelCase_		,							"""metrics.json"""							)
														snake_case_     							=		"""rouge2"""

														snake_case_     							=		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_							)
														snake_case_     							=		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							)
														snake_case_     							=		int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1							)
														self.assertEqual(len(metrics["""val"""]							)		,							UpperCAmelCase_							)







"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



