





"""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						: List[str]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]    ,       _lowercase						:    Union[str, Any]					) ->int:






	'''simple docstring'''






	return (preds == labels).mean()


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


@dataclass
class        __UpperCamelCase				:
							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=a__		,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)






def       _SCREAMING_SNAKE_CASE		(	) ->str:






	'''simple docstring'''






	a      :		Any	=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	a,       a,       a      :		Union[str, 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:
		a      :		Union[str, Any]	=				processors[data_args.task_name]()
		a      :		Any	=				processor.get_labels()
		a      :		Optional[Any]	=				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      :		List[Any]	=				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      :		Optional[int]	=				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      :		str	=				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      :		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
	)
	a      :		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						:    EvalPrediction					) -> Dict:
		a      :		int	=				np.argmax(p.predictions    ,       axis=1					)
		return {"acc": simple_accuracy(_lowercase    ,       p.label_ids					)}

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

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

		a      :		str	=				trainer.evaluate()

		a      :		List[str]	=				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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->Any:






	'''simple docstring'''






	main()


if __name__ == "__main__":
		main()










"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_UpperCamelCase		:  Any						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase       ):
      lowercase							    =       data
      lowercase							    =       None







    def __str__(			self       ):
      return F'{self.data}'


class        a     (   Generic[T]	):







    def __init__(			self       ):
      lowercase							    =       None







    def __iter__(			self       ):
      lowercase							    =       self.top
      while node:
        yield node.data
        lowercase							    =       node.next







    def __str__(			self       ):
      return "->".join([str(_lowerCamelCase       ) for item in self]       )







    def __len__(			self       ):
      return len(tuple(iter(self       )       )       )







    def 			UpperCamelCase_      (			self       ):
      return self.top is None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Node(_lowerCamelCase       )
      if not self.is_empty():
        lowercase							    =       self.top
      lowercase							    =       node







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('pop from empty stack'       )
      assert isinstance(self.top							,		_lowerCamelCase       )
      lowercase							    =       self.top
      lowercase							    =       self.top.next
      return pop_node.data







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('peek from empty stack'       )

      assert self.top is not None
      return self.top.data







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       None


if __name__ == "__main__":
    from doctest import testmod

    testmod()


