





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


__lowercase							:Dict							=	torch.cuda.is_available()
__lowercase							:List[str]							=	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase							:Tuple							=	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase							: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				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       "\n".join(_SCREAMING_SNAKE_CASE  )
						Path(_SCREAMING_SNAKE_CASE  ).open("w"  ).writelines(_SCREAMING_SNAKE_CASE  )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						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  (					__UpperCamelCase				):






							"""simple docstring"""

							@classmethod
							def   A_	(   cls					:  Dict     )						->List[str]:
													return cls

							@require_torch_multi_gpu
							def   A_	(   self					:  Optional[int]     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Tuple =       {
													    "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					:  List[Any]				,						a					:  Any				,						a					:  Dict=True     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {
													    "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     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       CHEAP_ARGS.copy()
													SCREAMING_SNAKE_CASE__  :					Any =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=__lowercase				,						output_dir=__lowercase				,						**__lowercase     )

													def convert(a					:  Any				,						a					:  Dict     ):
																			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}"""

													SCREAMING_SNAKE_CASE__  :					Dict =       [x for x in (convert(__lowercase				,						__lowercase     ) for k, v in args_d.items()) if len(__lowercase     )]
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       [sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
													execute_subprocess_async(__lowercase				,						env=self.get_env()     )

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

													SCREAMING_SNAKE_CASE__  :					int =       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     )
													SCREAMING_SNAKE_CASE__  :					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}"""]				,						__lowercase     )
													self.assertEqual(len(metrics["test"]     )				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1     )
													self.assertEqual(len(metrics["val"]     )				,						__lowercase     )







"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



