



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 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					return (preds == labels).mean()





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





@dataclass
class 					__lowerCAmelCase :
				__lowerCamelCase =				field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()       )}       )
				__lowerCamelCase =				field(metadata={'''help''': '''Should contain the data files for the task.'''}       )
				__lowerCamelCase =				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 =				field(
				    default=__a       ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
					_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    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			)

					# Set seed
					set_seed(training_args.seed			)

					try:
										_lowerCAmelCase						=    processors[data_args.task_name]()
										_lowerCAmelCase						=    processor.get_labels()
										_lowerCAmelCase						=    len(_a			)
					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.

					_lowerCAmelCase						=    AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,						num_labels=_a	,						finetuning_task=data_args.task_name	,						cache_dir=model_args.cache_dir	,						)
					_lowerCAmelCase						=    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	,						)
					_lowerCAmelCase						=    AutoModelForMultipleChoice.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
					_lowerCAmelCase						=    (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir	,						tokenizer=_a	,						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
					)
					_lowerCAmelCase						=    (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir	,						tokenizer=_a	,						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			) -> Dict:
										_lowerCAmelCase						=    np.argmax(p.predictions	,						axis=1			)
										return {"acc": simple_accuracy(_a	,						p.label_ids			)}

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

					# Initialize our Trainer
					_lowerCAmelCase						=    Trainer(
					    model=_a	,						args=_a	,						train_dataset=_a	,						eval_dataset=_a	,						compute_metrics=_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			)

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

										_lowerCAmelCase						=    trainer.evaluate()

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

																				results.update(_a			)

					return results


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					main()


if __name__ == "__main__":
	main()









'''simple docstring'''







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info





def 						snake_case_							(_a	:							Tuple     ):
    return {key.lstrip('''-'''     ): value for key, value in zip(unknown_args[::2]    ,   unknown_args[1::2]     )}





def 						snake_case_							():
    UpperCAmelCase			       =		ArgumentParser(
        '''HuggingFace Datasets CLI tool'''    ,   usage='''datasets-cli <command> [<args>]'''    ,   allow_abbrev=_a     )
    UpperCAmelCase			       =		parser.add_subparsers(help='''datasets-cli command helpers'''     )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a     )
    EnvironmentCommand.register_subcommand(_a     )
    TestCommand.register_subcommand(_a     )
    RunBeamCommand.register_subcommand(_a     )
    DummyDataCommand.register_subcommand(_a     )

    # Parse args
    UpperCAmelCase       ,     UpperCAmelCase			       =		parser.parse_known_args()
    if not hasattr(_a    ,   '''func'''     ):
        parser.print_help()
        exit(1     )
    UpperCAmelCase			       =		parse_unknown_args(_a     )

    # Run
    UpperCAmelCase			       =		args.func(_a    ,   **_a     )
    service.run()


if __name__ == "__main__":
   main()




