




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


lowerCAmelCase_					    = logging.getLogger(__name__)



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Tuple:
		return (preds == labels).mean()






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






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



def      __lowerCAmelCase			(					)     ->	Optional[int]:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowerCAmelCase__				:  Optional[Any]		       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
		lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  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'''	,      _lowerCamelCase      )

		# Set seed
		set_seed(training_args.seed      )

		try:
				lowerCAmelCase__				:  List[str]		       =     processors[data_args.task_name]()
				lowerCAmelCase__				:  Dict		       =     processor.get_labels()
				lowerCAmelCase__				:  Optional[int]		       =     len(_lowerCamelCase      )
		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__				:  Dict		       =     AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,      num_labels=_lowerCamelCase	,      finetuning_task=data_args.task_name	,      cache_dir=model_args.cache_dir	,      )
		lowerCAmelCase__				:  Optional[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	,      )
		lowerCAmelCase__				:  List[Any]		       =     AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path	,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )	,      config=_lowerCamelCase	,      cache_dir=model_args.cache_dir	,      )

		# Get datasets
		lowerCAmelCase__				:  Optional[Any]		       =     (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir	,      tokenizer=_lowerCamelCase	,      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__				:  Any		       =     (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir	,      tokenizer=_lowerCamelCase	,      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:
				lowerCAmelCase__				:  List[Any]		       =     np.argmax(p.predictions	,      axis=1      )
				return {"acc": simple_accuracy(_lowerCamelCase	,      p.label_ids      )}

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

		# Initialize our Trainer
		lowerCAmelCase__				:  Union[str, Any]		       =     Trainer(
		    model=_lowerCamelCase	,      args=_lowerCamelCase	,      train_dataset=_lowerCamelCase	,      eval_dataset=_lowerCamelCase	,      compute_metrics=_lowerCamelCase	,      data_collator=_lowerCamelCase	,      )

		# 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__				:  int		       =     {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''      )

				lowerCAmelCase__				:  Optional[int]		       =     trainer.evaluate()

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

								results.update(_lowerCamelCase      )

		return results



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[str]:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
					main()







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

