






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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	return (preds == labels).mean()


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


@dataclass
class 							__UpperCamelCase     :
	__snake_case						:str =							field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
	__snake_case						:str =							field(metadata={'help': 'Should contain the data files for the task.'} )
	__snake_case						:int =							field(
	    default=1_2_8 ,      metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    } ,      )
	__snake_case						:bool =							field(
	    default=_lowerCAmelCase ,      metadata={'help': 'Overwrite the cached training and evaluation sets'} )





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
	__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"""					,	lowerCamelCase       )

	# Set seed
	set_seed(training_args.seed       )

	try:
		__lowercase   							=	processors[data_args.task_name]()
		__lowercase   							=	processor.get_labels()
		__lowercase   							=	len(lowerCamelCase       )
	except KeyError:
		raise ValueError("""Task not found: %s""" % (data_args.task_name)       )

	# 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					,	num_labels=lowerCamelCase					,	finetuning_task=data_args.task_name					,	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					,	)
	__lowercase   							=	AutoModelForMultipleChoice.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
	__lowercase   							=	(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir					,	tokenizer=lowerCamelCase					,	task=data_args.task_name					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.train					,	)
	    if training_args.do_train
	    else None
	)
	__lowercase   							=	(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir					,	tokenizer=lowerCamelCase					,	task=data_args.task_name					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.dev					,	)
	    if training_args.do_eval
	    else None
	)

	def compute_metrics(lowerCamelCase       ) -> Dict:
		__lowercase   							=	np.argmax(p.predictions					,	axis=1       )
		return {"acc": simple_accuracy(lowerCamelCase					,	p.label_ids       )}

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

	# Initialize our Trainer
	__lowercase   							=	Trainer(
	    model=lowerCamelCase					,	args=lowerCamelCase					,	train_dataset=lowerCamelCase					,	eval_dataset=lowerCamelCase					,	compute_metrics=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       )

    # Evaluation
	__lowercase   							=	{}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""       )

		__lowercase   							=	trainer.evaluate()

		__lowercase   							=	os.path.join(training_args.output_dir					,	"""eval_results.txt"""       )
		if trainer.is_world_master():
			with open(lowerCamelCase					,	"""w"""       ) as writer:
				logger.info("""***** Eval results *****"""       )
				for key, value in result.items():
					logger.info("""  %s = %s"""					,	lowerCamelCase					,	lowerCamelCase       )
					writer.write("""%s = %s\n""" % (key, value)       )

				results.update(lowerCamelCase       )

	return results





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	main()


if __name__ == "__main__":
						main()








UpperCAmelCase_							=				8.31_44_62  # Unit - J mol-1 K-1





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
							from doctest import testmod

							testmod()