





'''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_		     = logging.getLogger(__name__)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Tuple:


      '''simple docstring'''


      return (preds == labels).mean()

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

     __snake_case					:			str				=    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()     )}     )
     __snake_case					:			str				=    field(metadata={"help": "Should contain the data files for the task."}     )
     __snake_case					:			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."
             )
         }							,   )
     __snake_case					:			bool				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "Overwrite the cached training and evaluation sets"}     )



def      __lowercase     (   )				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_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"""				,		_SCREAMING_SNAKE_CASE			)

      # Set seed
      set_seed(training_args.seed			)

      try:
            SCREAMING_SNAKE_CASE	=		processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE	=		processor.get_labels()
            SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)
      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.

      SCREAMING_SNAKE_CASE	=		AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,		num_labels=_SCREAMING_SNAKE_CASE				,		finetuning_task=data_args.task_name				,		cache_dir=model_args.cache_dir				,		)
      SCREAMING_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				,		)
      SCREAMING_SNAKE_CASE	=		AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path				,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)				,		config=_SCREAMING_SNAKE_CASE				,		cache_dir=model_args.cache_dir				,		)

      # Get datasets
      SCREAMING_SNAKE_CASE	=		(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir				,		tokenizer=_SCREAMING_SNAKE_CASE				,		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
      )
      SCREAMING_SNAKE_CASE	=		(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir				,		tokenizer=_SCREAMING_SNAKE_CASE				,		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(_SCREAMING_SNAKE_CASE			) -> Dict:
            SCREAMING_SNAKE_CASE	=		np.argmax(p.predictions				,		axis=1			)
            return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE				,		p.label_ids			)}

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE	=		Trainer(
          model=_SCREAMING_SNAKE_CASE				,		args=_SCREAMING_SNAKE_CASE				,		train_dataset=_SCREAMING_SNAKE_CASE				,		eval_dataset=_SCREAMING_SNAKE_CASE				,		compute_metrics=_SCREAMING_SNAKE_CASE				,		data_collator=_SCREAMING_SNAKE_CASE				,		)

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

            SCREAMING_SNAKE_CASE	=		trainer.evaluate()

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

                        results.update(_SCREAMING_SNAKE_CASE			)

      return results



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()









import os
from datetime import datetime as dt

from github import Github


SCREAMING_SNAKE_CASE_		     = [
    """good first issue""",
    """feature request""",
    """wip""",
]



def      __lowercase     (   )				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Github(os.environ["""GITHUB_TOKEN"""]			)
      SCREAMING_SNAKE_CASE	=		g.get_repo("""huggingface/accelerate"""			)
      SCREAMING_SNAKE_CASE	=		repo.get_issues(state="""open"""			)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE	=		sorted([comment for comment in issue.get_comments()]				,		key=lambda _SCREAMING_SNAKE_CASE			: i.created_at				,		reverse=_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		comments[0] if len(_SCREAMING_SNAKE_CASE			) > 0 else None
            SCREAMING_SNAKE_CASE	=		dt.utcnow()
            SCREAMING_SNAKE_CASE	=		(current_time - issue.updated_at).days
            SCREAMING_SNAKE_CASE	=		(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state="""closed"""			)
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
            ):
                  # Add stale comment
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""			)


if __name__ == "__main__":
      main()


