






"""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


_snake_case       :      List[Any]       =					torch.cuda.is_available()
_snake_case       :      Tuple       =					[" Sam ate lunch today.", "Sams lunch ingredients."]
_snake_case       :      Dict       =					["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_snake_case       :      str       =					{
    "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_				(  __A  :		Path			,	__A  :		list			):
					lowerCamelCase__				='\n'.join(__A			)
					Path(__A			).open('w'			).writelines(__A			)




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			):
					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        __lowercase(       _UpperCamelCase  ):






						@classmethod
						def 		lowerCAmelCase   (					cls)			->  int:




											"""simple docstring"""





											return cls






						@require_torch_multi_gpu
						def 		lowerCAmelCase   (					self)			->  Any:




											"""simple docstring"""





											lowerCamelCase__				={
											    '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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=True)			->  Union[str, Any]:




											"""simple docstring"""





											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(_SCREAMING_SNAKE_CASE)
											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=_SCREAMING_SNAKE_CASE    ,						output_dir=_SCREAMING_SNAKE_CASE    ,						**_SCREAMING_SNAKE_CASE)

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

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

											lowerCamelCase__				=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)
											lowerCamelCase__				=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)
											lowerCamelCase__				=int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1)
											self.assertEqual(len(metrics['val'])    ,						_SCREAMING_SNAKE_CASE)







"""simple docstring"""



from __future__ import annotations




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					assert n > 1 and a > 0 and greatest_common_divisor(__A			,	__A			) == 1
					((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	__A			)  # Implemented below
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	__A			)  # Implemented below
					if b < 0:
										lowerCamelCase__				=(b % n + n) % n
					return b




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					lowerCamelCase__				=invert_modulo(__A			,	__A			)
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					assert a >= 0 and b >= 0

					if b == 0:
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=a, 1, 0
					else:
										((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	a % b			)
										lowerCamelCase__				=q
										lowerCamelCase__				=p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if b == 0:
										return (1, 0)
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	a % b			)
					lowerCamelCase__				=a // b
					return (y, x - k * y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if a < b:
										lowerCamelCase__				,     lowerCamelCase__				=b, a

					while a % b != 0:
										lowerCamelCase__				,     lowerCamelCase__				=b, a % b

					return b


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

						testmod(name="modular_division", verbose=True)
						testmod(name="modular_division2", verbose=True)
						testmod(name="invert_modulo", verbose=True)
						testmod(name="extended_gcd", verbose=True)
						testmod(name="extended_euclid", verbose=True)
						testmod(name="greatest_common_divisor", verbose=True)