


'''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


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






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		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 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@classmethod
			def 				lowerCAmelCase__     (		cls	:  Tuple			) ->Optional[Any]:



					'''simple docstring'''







					return cls




			@require_torch_multi_gpu
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   {
					    "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 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Optional[Any]=True			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   {
					    "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__			)
					_UpperCAmelCase		:							dict             =   CHEAP_ARGS.copy()
					_UpperCAmelCase		:							Optional[Any]             =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
					_UpperCAmelCase		:							str             =   self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=lowerCamelCase__						,	output_dir=lowerCamelCase__						,	**lowerCamelCase__			)

					def convert(lowerCamelCase__	:  str						,	lowerCamelCase__	:  str			):
							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}"""

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

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

					_UpperCAmelCase		:							Optional[Any]             =   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__			)
					_UpperCAmelCase		:							Tuple             =   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}"""]						,	lowerCamelCase__			)
					self.assertEqual(len(metrics["test"]			)						,	1			)
					_UpperCAmelCase		:							str             =   int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1			)
					self.assertEqual(len(metrics["val"]			)						,	lowerCamelCase__			)







'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



