



"""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


__a      =				logging.getLogger(__name__)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						return (preds == labels).mean()
@dataclass
class 				lowerCamelCase  :






					'''simple docstring'''





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

@dataclass
class 				lowerCamelCase  :






					'''simple docstring'''





					_A		:			str						=     field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()				)}				)
					_A		:			str						=     field(metadata={"""help""": """Should contain the data files for the task."""}				)
					_A		:			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."""
					        )
					    }    ,						)
					_A		:			bool						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)

def    A_  (			):







						'''simple docstring'''


						snake_case_ :List[str]						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
						snake_case_,       snake_case_,       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""",					_lowercase	)

						# Set seed
						set_seed(training_args.seed	)

						try:
												snake_case_ :Optional[Any]						=			processors[data_args.task_name]()
												snake_case_ :Tuple						=			processor.get_labels()
												snake_case_ :int						=			len(_lowercase	)
						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_ :List[str]						=			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,					num_labels=_lowercase,					finetuning_task=data_args.task_name,					cache_dir=model_args.cache_dir,					)
						snake_case_ :Union[str, 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,					)
						snake_case_ :Union[str, Any]						=			AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path	),					config=_lowercase,					cache_dir=model_args.cache_dir,					)

						# Get datasets
						snake_case_ :List[str]						=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir,					tokenizer=_lowercase,					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_ :Optional[Any]						=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir,					tokenizer=_lowercase,					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(_lowercase	) -> Dict:
												snake_case_ :Union[str, Any]						=			np.argmax(p.predictions,					axis=1	)
												return {"acc": simple_accuracy(_lowercase,					p.label_ids	)}

						# Data collator
						snake_case_ :List[str]						=			DataCollatorWithPadding(_lowercase,					pad_to_multiple_of=8	) if training_args.fpaa else None

						# Initialize our Trainer
						snake_case_ :Optional[Any]						=			Trainer(
						    model=_lowercase,					args=_lowercase,					train_dataset=_lowercase,					eval_dataset=_lowercase,					compute_metrics=_lowercase,					data_collator=_lowercase,					)

						# 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_ :Optional[int]						=			{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""	)

												snake_case_ :List[str]						=			trainer.evaluate()

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

																								results.update(_lowercase	)

						return results
def    A_  (			_lowercase	):







						'''simple docstring'''


						main()


if __name__ == "__main__":
						main()





"""simple docstring"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
