

"""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


a__ :	Union[str, Any]						= torch.cuda.is_available()
a__ :	List[Any]						= [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__ :	int						= ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__ :	Optional[int]						= {
    """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   (    a__: Path ,	a__: list			)				->			Any:




					"""simple docstring"""



					__lowercase												=			'''\n'''.join(a__			)
					Path(a__			).open('''w'''			).writelines(a__			)





def 				__UpperCamelCase   (    a__: Tuple			)				->			Optional[Any]:




					"""simple docstring"""



					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 			_snake_case     (						UpperCamelCase       ):







						@classmethod
						def        _UpperCamelCase    (cls							)			->						Optional[int]:
											return cls







						@require_torch_multi_gpu
						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			{
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_     ,							check_contents=UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=True							)			->						Union[str, Any]:
											__lowercase												=			{
											    '''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(UpperCAmelCase_							)
											__lowercase												=			CHEAP_ARGS.copy()
											__lowercase												=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											__lowercase												=			self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_     ,							output_dir=UpperCAmelCase_     ,							**UpperCAmelCase_							)

											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}'''

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

											__lowercase												=			os.listdir(UpperCAmelCase_							)
											__lowercase												=			{os.path.basename(UpperCAmelCase_							) for p in contents}
											__lowercase												=			[p for p in contents if p.endswith('''ckpt'''							)]
											assert len(UpperCAmelCase_							) > 0

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

											# get the following from the module, (we don't have access to `model` here)
											__lowercase												=			os.path.join(UpperCAmelCase_     ,							'''metrics.json'''							)
											__lowercase												=			'''rouge2'''

											__lowercase												=			load_json(UpperCAmelCase_							)
											# {'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(UpperCAmelCase_							)
											__lowercase												=			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}''']     ,							UpperCAmelCase_							)
											self.assertEqual(len(metrics['''test''']							)     ,							1							)
											__lowercase												=			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1							)
											self.assertEqual(len(metrics['''val''']							)     ,							UpperCAmelCase_							)





"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


a__ :	List[Any]						= logging.getLogger(__name__)


@dataclass
class 			_snake_case     :
						__snake_case 			=     field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						__snake_case 			=     field(default=UpperCamelCase						,			metadata={'help': 'Set this flag to use fast tokenization.'}       )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,			)





def 				__UpperCamelCase   (    )				->			Union[str, Any]:




					"""simple docstring"""



					__lowercase												=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

					if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir			)
					    and os.listdir(training_args.output_dir			)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ''' --overwrite_output_dir to overcome.'''			)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,	datefmt='''%m/%d/%Y %H:%M:%S''' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1			) ,	training_args.fpaa ,	)
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank			):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s''' ,	a__			)

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowercase												=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
					__lowercase												=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	use_fast=a__ ,	)
					__lowercase												=			AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,	config=a__ ,	cache_dir=model_args.cache_dir ,	)

					# Get datasets
					__lowercase												=			hasattr(model.config ,	'''lang2id'''			)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir			)
					    if training_args.do_train
					    else None
					)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	mode='''dev''' ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir ,	)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					__lowercase												=			DataCollatorWithPadding(a__ ,	pad_to_multiple_of=8			) if training_args.fpaa else None

					# Initialize our Trainer
					__lowercase												=			Trainer(
					    model=a__ ,	args=a__ ,	train_dataset=a__ ,	eval_dataset=a__ ,	data_collator=a__ ,	)

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir			)





def 				__UpperCamelCase   (    a__: Any			)				->			Any:




					"""simple docstring"""



					main()


if __name__ == "__main__":
							main()


