



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": 4_2,
    "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": 1_2,
    "max_target_length": 1_2,
    "val_max_target_length": 1_2,
    "test_max_target_length": 1_2,
    "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 	a						(				_UpperCAmelCase							: Path			,       _UpperCAmelCase							: list						):







		'''simple docstring'''





		__UpperCAmelCase   :					Tuple													=     '''\n'''.join(_UpperCAmelCase						)
		Path(_UpperCAmelCase						).open('''w'''						).writelines(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





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

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			@classmethod
			def 			snake_case__					(       cls							:			int			):
					'''simple docstring'''





					return cls




			@require_torch_multi_gpu
			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     {
					    '''no_teacher''': True,
					    '''freeze_encoder''': True,
					    '''gpus''': 2,
					    '''overwrite_output_dir''': True,
					    '''sortish_sampler''': True,
					}
					self._test_distiller_cli_fork(a_	,	check_contents=a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			List[Any]	,	a_							:			Optional[int]=True			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     {
					    '''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(a_			)
					__UpperCAmelCase   :					dict													=     CHEAP_ARGS.copy()
					__UpperCAmelCase   :					Dict													=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
					__UpperCAmelCase   :					Tuple													=     self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=a_	,	output_dir=a_	,	**a_			)

					def convert(a_							:			int	,	a_							:			Optional[int]			):
							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(a_	,	a_			) for k, v in args_d.items()) if len(a_			)]
					__UpperCAmelCase   :					List[str]													=     [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
					execute_subprocess_async(a_	,	env=self.get_env()			)

					__UpperCAmelCase   :					Any													=     os.listdir(a_			)
					__UpperCAmelCase   :					Optional[int]													=     {os.path.basename(a_			) for p in contents}
					__UpperCAmelCase   :					str													=     [p for p in contents if p.endswith('''ckpt'''			)]
					assert len(a_			) > 0

					self.assertIn('''test_generations.txt'''	,	a_			)
					self.assertIn('''test_results.txt'''	,	a_			)

					# get the following from the module, (we don't have access to `model` here)
					__UpperCAmelCase   :					Optional[Any]													=     os.path.join(a_	,	'''metrics.json'''			)
					__UpperCAmelCase   :					Optional[Any]													=     '''rouge2'''

					__UpperCAmelCase   :					Tuple													=     load_json(a_			)
					# {'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(a_			)
					__UpperCAmelCase   :					List[str]													=     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}']	,	a_			)
					self.assertEqual(len(metrics['''test''']			)	,	1			)
					__UpperCAmelCase   :					Any													=     int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1			)
					self.assertEqual(len(metrics['''val''']			)	,	a_			)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A             =logging.get_logger(__name__)

__A             ={
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"""splinter"""




			def __init__(       self							:			Any	,	a_							:			Optional[int]=3_05_22	,	a_							:			Union[str, Any]=7_68	,	a_							:			int=12	,	a_							:			int=12	,	a_							:			List[Any]=30_72	,	a_							:			Union[str, Any]="gelu"	,	a_							:			Any=0.1	,	a_							:			Union[str, Any]=0.1	,	a_							:			Any=5_12	,	a_							:			str=2	,	a_							:			Dict=0.0_2	,	a_							:			int=1e-12	,	a_							:			str=True	,	a_							:			Tuple=0	,	a_							:			List[Any]=1_04	,	**a_							:			str	,	):
					'''simple docstring'''





					super().__init__(pad_token_id=a_	,	**a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     vocab_size
					__UpperCAmelCase   :					List[Any]													=     max_position_embeddings
					__UpperCAmelCase   :					str													=     hidden_size
					__UpperCAmelCase   :					Union[str, Any]													=     num_hidden_layers
					__UpperCAmelCase   :					Any													=     num_attention_heads
					__UpperCAmelCase   :					Any													=     intermediate_size
					__UpperCAmelCase   :					Tuple													=     hidden_act
					__UpperCAmelCase   :					str													=     hidden_dropout_prob
					__UpperCAmelCase   :					Any													=     attention_probs_dropout_prob
					__UpperCAmelCase   :					Optional[int]													=     initializer_range
					__UpperCAmelCase   :					Any													=     type_vocab_size
					__UpperCAmelCase   :					int													=     layer_norm_eps
					__UpperCAmelCase   :					List[Any]													=     use_cache
					__UpperCAmelCase   :					Optional[int]													=     question_token_id





