"""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': 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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Union[str, Any]:




				'''simple docstring'''



				lowerCamelCase__ ="\n".join(_A    )
				Path(_A    ).open("w"    ).writelines(_A    )





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Any:




				'''simple docstring'''



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






class        __UpperCAmelCase						(     lowerCamelCase__     ):

				@classmethod
				def 		_a     (				cls				):
								return cls

				@require_torch_multi_gpu
				def 		_a     (				self				):
								lowerCamelCase__ ={
								    "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     ,			_lowerCamelCase     ,			_lowerCamelCase=True				):
								lowerCamelCase__ ={
								    "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_				)
								lowerCamelCase__ =CHEAP_ARGS.copy()
								lowerCamelCase__ =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
								lowerCamelCase__ =self.get_auto_remove_tmp_dir()
								args_d.update(data_dir=lowercase_     ,			output_dir=lowercase_     ,			**lowercase_				)

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

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

								lowerCamelCase__ =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_				)
								lowerCamelCase__ =metrics["val"][-1]
								self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]     ,			0.0_1				)
								self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']     ,			lowercase_				)
								self.assertEqual(len(metrics["test"]				)     ,			1				)
								lowerCamelCase__ =int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1				)
								self.assertEqual(len(metrics["val"]				)     ,			lowercase_				)



"""simple docstring"""


from __future__ import annotations

from collections.abc import Generator





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Generator[int, None, None]:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ) or n < 1:
												raise Exception("Sequence only defined for positive integers"    )

						yield n
						while n != 1:
												if n % 2 == 0:
																		n //= 2
												else:
																		lowerCamelCase__ =3 * n + 1
												yield n





def       lowerCamelCase_			( )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =43
						lowerCamelCase__ =tuple(collatz_sequence(__lowerCAmelCase    )    )
						print(__lowerCAmelCase    )
						print(F'''Collatz sequence from {n} took {len(__lowerCAmelCase    )} steps.'''    )


if __name__ == "__main__":
	main()


