

"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


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





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()





"""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()


