


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_     :       Union[str, Any]   							=logging.getLogger(__name__)




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




						return (preds == labels).mean()





@dataclass
class 							lowercase_					:






							"""simple docstring"""







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





@dataclass
class 							lowercase_					:






							"""simple docstring"""







							snake_case_										=		field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys())})
							snake_case_										=		field(metadata={'''help''': '''Should contain the data files for the task.'''})
							snake_case_										=		field(
							    default=1_28      ,metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }      ,)
							snake_case_										=		field(
							    default=a__      ,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})




def 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
						a_		      =		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:
												a_		      =		processors[data_args.task_name]()
												a_		      =		processor.get_labels()
												a_		      =		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.

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

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

						# Initialize our Trainer
						a_		      =		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
						a_		      =		{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""   )

												a_		      =		trainer.evaluate()

												a_		      =		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 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




						main()


if __name__ == "__main__":
	main()





'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



