






"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__							= logging.getLogger()





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Any							=						argparse.ArgumentParser()
				parser.add_argument('-f'					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						parser.parse_args()
				return args.f






class _lowerCAmelCase	(						__UpperCAmelCase					):







						def  A			(						self       )		->       Any:
										_SCREAMING_SNAKE_CASE      :		Dict							=						logging.StreamHandler(sys.stdout       )
										logger.addHandler(A_       )

										_SCREAMING_SNAKE_CASE      :		Any							=						self.get_auto_remove_tmp_dir()
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						F"""\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            """.split()

										with patch.object(A_      ,							'argv'      ,							A_       ):
														_SCREAMING_SNAKE_CASE      :		List[str]							=						run_glue_with_pabee.main()
														for value in result.values():
																		self.assertGreaterEqual(A_      ,							0.75       )









"""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


lowerCAmelCase__							= logging.getLogger(__name__)


@dataclass
class _lowerCAmelCase	:
						SCREAMING_SNAKE_CASE_:				str   				=							field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				bool   				=							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.
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )







def 							lowercase__		(				):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						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.
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						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',	lowerCamelCase					)

				# 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.

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						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,	)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						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=lowerCamelCase,	)
				_SCREAMING_SNAKE_CASE      :		Dict							=						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path,	from_tf=bool('.ckpt' in model_args.model_name_or_path					),	config=lowerCamelCase,	cache_dir=model_args.cache_dir,	)

				# Get datasets
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						hasattr(model.config,	'lang2id'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir					)
				    if training_args.do_train
				    else None
				)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	mode='dev',	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir,	)
				    if training_args.do_eval
				    else None
				)

				# Data collator
				_SCREAMING_SNAKE_CASE      :		int							=						DataCollatorWithPadding(lowerCamelCase,	pad_to_multiple_of=8					) if training_args.fpaa else None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Trainer(
				    model=lowerCamelCase,	args=lowerCamelCase,	train_dataset=lowerCamelCase,	eval_dataset=lowerCamelCase,	data_collator=lowerCamelCase,	)

				# 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 							lowercase__		(				lowerCamelCase					):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()

