






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


__UpperCAmelCase										=		torch.cuda.is_available()
__UpperCAmelCase										=		[' Sam ate lunch today.', 'Sams lunch ingredients.']
__UpperCAmelCase										=		['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__UpperCAmelCase										=		{
    '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   lowercase__   (  __snake_case					:							int  , __snake_case					:							Dict						):


					'''simple docstring'''


					UpperCAmelCase_					:   Tuple   =		'\n'.join(lowercase__						)
					Path(lowercase__						).open('w'						).writelines(lowercase__						)




def   lowercase__   (  __snake_case					:							str						):


					'''simple docstring'''


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






class 						lowerCamelCase   (__UpperCAmelCase    ):







		'''simple docstring'''



		@classmethod
		def       __UpperCAmelCase    (							cls     )			->							Any:
							return cls



		@require_torch_multi_gpu
		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							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       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase=True     )			->							Dict:
							UpperCAmelCase_					:   str   =		{
							    '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_					:   int   =		CHEAP_ARGS.copy()
							UpperCAmelCase_					:   int   =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
							UpperCAmelCase_					:   Union[str, Any]   =		self.get_auto_remove_tmp_dir()
							args_d.update(data_dir=lowerCamelCase__       ,	output_dir=lowerCamelCase__       ,	**lowerCamelCase__     )

							def convert(_UpperCamelCase       ,	_UpperCamelCase     ):
												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_					:   Optional[Any]   =		[x for x in (convert(lowerCamelCase__       ,	lowerCamelCase__     ) for k, v in args_d.items()) if len(lowerCamelCase__     )]
							UpperCAmelCase_					:   List[str]   =		[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
							execute_subprocess_async(lowerCamelCase__       ,	env=self.get_env()     )

							UpperCAmelCase_					:   Union[str, Any]   =		os.listdir(lowerCamelCase__     )
							UpperCAmelCase_					:   str   =		{os.path.basename(lowerCamelCase__     ) for p in contents}
							UpperCAmelCase_					:   Tuple   =		[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_					:   int   =		os.path.join(lowerCamelCase__       ,	'metrics.json'     )
							UpperCAmelCase_					:   str   =		'rouge2'

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














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






