

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


lowercase_									=   logging.getLogger(__name__)
def      a      (			A__	:   List[Any]      ,   A__	:   Tuple     )    ->      Optional[int]:





      """simple docstring"""



      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=__lowerCamelCase			,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
  _a									=  field(
      default=__lowerCamelCase			,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
  _a									=  field(
      default=__lowerCamelCase			,			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=__lowerCamelCase			,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)
def      a      (			)    ->      Any:





      """simple docstring"""



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

      _lowercase	       =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      ,   )
      _lowercase	       =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      ,   )
      _lowercase	       =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
      _lowercase	       =(
          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
      )
      _lowercase	       =(
          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(A__	:   List[Any]     ) -> Dict:
            _lowercase	       =np.argmax(p.predictions      ,   axis=1     )
            return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE      ,   p.label_ids     )}

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

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

            _lowercase	       =trainer.evaluate()

            _lowercase	       =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      a      (			A__	:   List[Any]     )    ->      List[Any]:





      """simple docstring"""



      main()


if __name__ == "__main__":
  main()




import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase	      = [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]






def 							UpperCAmelCase_		(							):
	lowercase       =    Github(os.environ['GITHUB_TOKEN']							)
	lowercase       =    g.get_repo('huggingface/diffusers'							)
	lowercase       =    repo.get_issues(state='open'							)

	for issue in open_issues:
		lowercase       =    sorted(issue.get_comments() ,							key=lambda __SCREAMING_SNAKE_CASE							: i.created_at ,							reverse=__SCREAMING_SNAKE_CASE							)
		lowercase       =    comments[0] if len(__SCREAMING_SNAKE_CASE							) > 0 else None
		if (
		    last_comment is not None
		    and last_comment.user.login == "github-actions[bot]"
		    and (dt.utcnow() - issue.updated_at).days > 7
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Closes the issue after 7 days of inactivity since the Stalebot notification.
			issue.edit(state='closed'							)
		elif (
		    "stale" in issue.get_labels()
		    and last_comment is not None
		    and last_comment.user.login != "github-actions[bot]"
		):
			# Opens the issue if someone other than Stalebot commented.
			issue.edit(state='open'							)
			issue.remove_from_labels('stale'							)
		elif (
		    (dt.utcnow() - issue.updated_at).days > 23
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Post a Stalebot notification after 23 days of inactivity.
			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/diffusers/blob/main/CONTRIBUTING.md) '
			    'are likely to be ignored.'							)
			issue.add_to_labels('stale'							)


if __name__ == "__main__":
	main()

