




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Dict						=			logging.getLogger(__name__)


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Tuple					):

							'''simple docstring'''






							return (preds == labels).mean()




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




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				str							=   field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()							)}							)
			lowerCAmelCase_					:				str							=   field(metadata={"help": "Should contain the data files for the task."}							)
			lowerCAmelCase_					:				int							=   field(
			    default=128						,						metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }						,						)
			lowerCAmelCase_					:				bool							=   field(
			    default=lowercase_						,						metadata={"help": "Overwrite the cached training and evaluation sets"}							)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
							snake_case_ ,				snake_case_ ,				snake_case_      =				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"							,    snake_case					)

							# Set seed
							set_seed(training_args.seed					)

							try:
														snake_case_      =				processors[data_args.task_name]()
														snake_case_      =				processor.get_labels()
														snake_case_      =				len(snake_case					)
							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.

							snake_case_      =				AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,    num_labels=snake_case							,    finetuning_task=data_args.task_name							,    cache_dir=model_args.cache_dir							,    )
							snake_case_      =				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							,    )
							snake_case_      =				AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path							,    from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,    config=snake_case							,    cache_dir=model_args.cache_dir							,    )

							# Get datasets
							snake_case_      =				(
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,    tokenizer=snake_case							,    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
							)
							snake_case_      =				(
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,    tokenizer=snake_case							,    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(snake_case							:	EvalPrediction					) -> Dict:
														snake_case_      =				np.argmax(p.predictions							,    axis=1					)
														return {"acc": simple_accuracy(snake_case							,    p.label_ids					)}

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

							# Initialize our Trainer
							snake_case_      =				Trainer(
							    model=snake_case							,    args=snake_case							,    train_dataset=snake_case							,    eval_dataset=snake_case							,    compute_metrics=snake_case							,    data_collator=snake_case							,    )

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

														snake_case_      =				trainer.evaluate()

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

																												results.update(snake_case					)

							return results


def 					UpperCamelCase_(     snake_case							:	Dict					):

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






							while b:
														snake_case_ ,				snake_case_      =				b, a % b
							return a


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






							return a if b == 0 else euclidean_gcd_recursive(snake_case							,    a % b					)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3							,    5					)}'					)
							print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5							,    3					)}'					)
							print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1							,    3					)}'					)
							print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3							,    6					)}'					)
							print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6							,    3					)}'					)

							print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3							,    5					)}'					)
							print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5							,    3					)}'					)
							print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1							,    3					)}'					)
							print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3							,    6					)}'					)
							print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6							,    3					)}'					)


if __name__ == "__main__":
		main()




