




'''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							:   Optional[Any]      		= logging.getLogger(__name__)


def      UpperCamelCase_(	snake_case							:       Union[str, Any]							,				snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							return (preds == labels).mean()




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




@dataclass
class  _snake_case		:
			lowerCAmelCase_     :      Optional[Any]						       =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()							)}							)
			lowerCAmelCase_     :      List[Any]						       =    field(metadata={"help": "Should contain the data files for the task."}							)
			lowerCAmelCase_     :      Tuple						       =    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_     :      Any						       =    field(
			    default=UpperCamelCase_						,						metadata={"help": "Overwrite the cached training and evaluation sets"}							)


def      UpperCamelCase_(	):

							'''simple docstring'''






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

							# Set seed
							set_seed(training_args.seed     )

							try:
														snake_case_						=      processors[data_args.task_name]()
														snake_case_						=      processor.get_labels()
														snake_case_						=      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_						=      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_						=      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=_lowercase							,				cache_dir=model_args.cache_dir							,				)

							# Get datasets
							snake_case_						=      (
							    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_						=      (
							    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(snake_case							:       Optional[int]     ) -> Dict:
														snake_case_						=      np.argmax(p.predictions							,				axis=1     )
														return {"acc": simple_accuracy(_lowercase							,				p.label_ids     )}

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

							# Initialize our Trainer
							snake_case_						=      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_						=      {}
							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(_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      UpperCamelCase_(	snake_case							:       str     ):

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()





def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





