

"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 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()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  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()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   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/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()








"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__UpperCAmelCase =				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__UpperCAmelCase =				{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__UpperCAmelCase =				{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__UpperCAmelCase =				sorted(arg_to_scheduler.keys())
__UpperCAmelCase =				'{' + ', '.join(arg_to_scheduler_choices) + '}'







class      __lowercase			(    pl.LightningModule					):

  def __init__(  self		:			List[str]					,A		:			argparse.Namespace					,A		:			List[Any]=None					,A		:			Any="base"					,A		:			List[str]=None					,A		:			Optional[Any]=None					,A		:			int=None					,**A		:			Union[str, Any]					,):






   '''simple docstring'''
   super().__init__()
   # TODO: move to self.save_hyperparameters()
   # self.save_hyperparameters()
   # can also expand arguments into trainer signature for easier reading

   self.save_hyperparameters(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					0
   UpperCAmelCase__					:  List[Any]					   =					Path(self.hparams.output_dir    )
   UpperCAmelCase__					:  str					   =					self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
    UpperCAmelCase__					:  Any					   =					AutoConfig.from_pretrained(
        self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,**({"""num_labels""": num_labels} if num_labels is not None else {})					,cache_dir=A					,**A					,)
   else:
    UpperCAmelCase__					:  PretrainedConfig					   =					config

   UpperCAmelCase__					:  Optional[int]					   =					("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
   for p in extra_model_params:
    if getattr(self.hparams					,A					,A    ):
     assert hasattr(self.config					,A    ), f"model config doesn't have a `{p}` attribute"
     setattr(self.config					,A					,getattr(self.hparams					,A    )    )

   if tokenizer is None:
    UpperCAmelCase__					:  Dict					   =					AutoTokenizer.from_pretrained(
        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,cache_dir=A					,)
   else:
    UpperCAmelCase__					:  PreTrainedTokenizer					   =					tokenizer
   UpperCAmelCase__					:  Optional[int]					   =					MODEL_MODES[mode]
   if model is None:
    UpperCAmelCase__					:  Optional[Any]					   =					self.model_type.from_pretrained(
        self.hparams.model_name_or_path					,from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path    )					,config=self.config					,cache_dir=A					,)
   else:
    UpperCAmelCase__					:  List[Any]					   =					model

  def    __lowercase       (  self		:			Optional[int]					,*A		:			int					,**A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					self.model_type.from_pretrained(*A					,**A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					arg_to_scheduler[self.hparams.lr_scheduler]
   UpperCAmelCase__					:  Optional[int]					   =					get_schedule_func(
       self.opt					,num_warmup_steps=self.hparams.warmup_steps					,num_training_steps=self.total_steps()    )
   UpperCAmelCase__					:  Any					   =					{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
   return scheduler

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.model
   UpperCAmelCase__					:  Any					   =					["""bias""", """LayerNorm.weight"""]
   UpperCAmelCase__					:  str					   =					[
       {
           """params""": [
               p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )
           ],  # check this named paramters
           """weight_decay""": self.hparams.weight_decay,
       },
       {
           """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
           """weight_decay""": 0.0,
       },
   ]
   if self.hparams.adafactor:
    UpperCAmelCase__					:  List[str]					   =					Adafactor(
        A					,lr=self.hparams.learning_rate					,scale_parameter=A					,relative_step=A    )

   else:
    UpperCAmelCase__					:  Tuple					   =					AdamW(
        A					,lr=self.hparams.learning_rate					,eps=self.hparams.adam_epsilon    )
   UpperCAmelCase__					:  Tuple					   =					optimizer

   UpperCAmelCase__					:  Optional[int]					   =					self.get_lr_scheduler()

   return [optimizer], [scheduler]

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[str]					,A		:			int    ):






   '''simple docstring'''
   return self.validation_step(A					,A    )

  def    __lowercase       (  self		:			Any					,A		:			Tuple    ):






   '''simple docstring'''
   return self.validation_end(A    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					max(1					,self.hparams.gpus    )  # TODO: consider num_tpu_cores
   UpperCAmelCase__					:  Union[str, Any]					   =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
   return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

  def    __lowercase       (  self		:			List[Any]					,A		:			Optional[int]    ):






   '''simple docstring'''
   if stage == "test":
    UpperCAmelCase__					:  Tuple					   =					len(self.test_dataloader().dataset    )
   else:
    UpperCAmelCase__					:  Any					   =					self.get_dataloader("""train"""					,self.hparams.train_batch_size					,shuffle=A    )
    UpperCAmelCase__					:  int					   =					len(self.train_dataloader().dataset    )

  def    __lowercase       (  self		:			Dict					,A		:			str					,A		:			int					,A		:			bool = False    ):






   '''simple docstring'''
   raise NotImplementedError("""You must implement this for your task"""    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   return self.train_loader

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return self.get_dataloader("""dev"""					,self.hparams.eval_batch_size					,shuffle=A    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return self.get_dataloader("""test"""					,self.hparams.eval_batch_size					,shuffle=A    )

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[int]    ):






   '''simple docstring'''
   return os.path.join(
       self.hparams.data_dir					,"""cached_{}_{}_{}""".format(
           A					,list(filter(A					,self.hparams.model_name_or_path.split("""/"""    )    )    ).pop()					,str(self.hparams.max_seq_length    )					,)					,)

  @pl.utilities.rank_zero_only
  def    __lowercase       (  self		:			Tuple					,A		:			Dict[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.output_dir.joinpath("""best_tfmr"""    )
   UpperCAmelCase__					:  int					   =					self.step_count
   self.model.save_pretrained(A    )
   self.tokenizer.save_pretrained(A    )




  @staticmethod
  def    __lowercase       (  A		:			Any					,A		:			Optional[int]    ):






   '''simple docstring'''
   parser.add_argument(
       """--model_name_or_path"""					,default=A					,type=A					,required=A					,help="""Path to pretrained model or model identifier from huggingface.co/models"""					,)
   parser.add_argument(
       """--config_name"""					,default=""""""					,type=A					,help="""Pretrained config name or path if not the same as model_name"""    )
   parser.add_argument(
       """--tokenizer_name"""					,default=A					,type=A					,help="""Pretrained tokenizer name or path if not the same as model_name"""					,)
   parser.add_argument(
       """--cache_dir"""					,default=str(Path(A    ).parent / """test_run""" / """cache"""    )					,type=A					,help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""					,)
   parser.add_argument(
       """--encoder_layerdrop"""					,type=A					,help="""Encoder layer dropout probability (Optional). Goes into model.config"""					,)
   parser.add_argument(
       """--decoder_layerdrop"""					,type=A					,help="""Decoder layer dropout probability (Optional). Goes into model.config"""					,)
   parser.add_argument(
       """--dropout"""					,type=A					,help="""Dropout probability (Optional). Goes into model.config"""					,)
   parser.add_argument(
       """--attention_dropout"""					,type=A					,help="""Attention dropout probability (Optional). Goes into model.config"""					,)
   parser.add_argument("""--learning_rate"""					,default=5e-5					,type=A					,help="""The initial learning rate for Adam."""    )
   parser.add_argument(
       """--lr_scheduler"""					,default="""linear"""					,choices=A					,metavar=A					,type=A					,help="""Learning rate scheduler"""					,)
   parser.add_argument("""--weight_decay"""					,default=0.0					,type=A					,help="""Weight decay if we apply some."""    )
   parser.add_argument("""--adam_epsilon"""					,default=1e-8					,type=A					,help="""Epsilon for Adam optimizer."""    )
   parser.add_argument("""--warmup_steps"""					,default=0					,type=A					,help="""Linear warmup over warmup_steps."""    )
   parser.add_argument("""--num_workers"""					,default=4					,type=A					,help="""kwarg passed to DataLoader"""    )
   parser.add_argument("""--num_train_epochs"""					,dest="""max_epochs"""					,default=3					,type=A    )
   parser.add_argument("""--train_batch_size"""					,default=32					,type=A    )
   parser.add_argument("""--eval_batch_size"""					,default=32					,type=A    )
   parser.add_argument("""--adafactor"""					,action="""store_true"""    )







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			List[Any]					,A		:			Any					,A		:			Dict    ):






   '''simple docstring'''
   if (
       trainer.is_global_zero and trainer.global_rank == 0
   ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
    pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int    ):






   '''simple docstring'''
   # print(pl_module.model.rag)
   for name, param in pl_module.model.rag.named_parameters():
    if param.grad is None:
     print(A    )







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Tuple					,A		:			Dict					,A		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					trainer.lr_schedulers[0]["""scheduler"""]
   UpperCAmelCase__					:  int					   =					{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
   pl_module.logger.log_metrics(A    )

  def    __lowercase       (  self		:			List[str]					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






   '''simple docstring'''
   rank_zero_info("""***** Validation results *****"""    )
   UpperCAmelCase__					:  List[Any]					   =					trainer.callback_metrics
   # Log results
   for key in sorted(A    ):
    if key not in ["log", "progress_bar"]:
     rank_zero_info("""{} = {}\n""".format(A					,str(metrics[key]    )    )    )




  def    __lowercase       (  self		:			Tuple					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






   '''simple docstring'''
   rank_zero_info("""***** Test results *****"""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					trainer.callback_metrics
   # Log and save results to file
   UpperCAmelCase__					:  Any					   =					os.path.join(pl_module.hparams.output_dir					,"""test_results.txt"""    )
   with open(A					,"""w"""    ) as writer:
    for key in sorted(A    ):
     if key not in ["log", "progress_bar"]:
      rank_zero_info("""{} = {}\n""".format(A					,str(metrics[key]    )    )    )
      writer.write("""{} = {}\n""".format(A					,str(metrics[key]    )    )    )







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 parser.add_argument(
     """--output_dir"""							,    default=str(Path(__UpperCamelCase      ).parent / """test_run""" / """model_checkpoints"""      )							,    type=__UpperCamelCase							,    help="""The output directory where the model predictions and checkpoints will be written."""							,    )
 parser.add_argument(
     """--fp16"""							,    action="""store_true"""							,    help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""							,    )

 parser.add_argument(
     """--fp16_opt_level"""							,    type=__UpperCamelCase							,    default="""O2"""							,    help=(
         """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
         """See details at https://nvidia.github.io/apex/amp.html"""
     )							,    )
 parser.add_argument("""--n_tpu_cores"""							,    dest="""tpu_cores"""							,    type=__UpperCamelCase      )
 parser.add_argument("""--max_grad_norm"""							,    dest="""gradient_clip_val"""							,    default=1.0							,    type=__UpperCamelCase							,    help="""Max gradient norm"""      )
 parser.add_argument("""--do_train"""							,    action="""store_true"""							,    help="""Whether to run training."""      )
 parser.add_argument("""--do_predict"""							,    action="""store_true"""							,    help="""Whether to run predictions on the test set."""      )
 parser.add_argument(
     """--gradient_accumulation_steps"""							,    dest="""accumulate_grad_batches"""							,    type=__UpperCamelCase							,    default=1							,    help="""Number of updates steps to accumulate before performing a backward/update pass."""							,    )
 parser.add_argument("""--seed"""							,    type=__UpperCamelCase							,    default=42							,    help="""random seed for initialization"""      )
 parser.add_argument(
     """--data_dir"""							,    default=str(Path(__UpperCamelCase      ).parent / """test_run""" / """dummy-train-data"""      )							,    type=__UpperCamelCase							,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""							,    )




def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=None							,    __UpperCamelCase=True							,    __UpperCamelCase=[]							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    **__UpperCamelCase							,    ):
 '''simple docstring'''





 pl.seed_everything(args.seed      )

 # init model
 UpperCAmelCase__					:  Optional[int]					   =					Path(model.hparams.output_dir      )
 odir.mkdir(exist_ok=__UpperCamelCase      )

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCAmelCase__					:  List[Any]					   =					pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir							,    prefix="""checkpoint"""							,    monitor="""val_loss"""							,    mode="""min"""							,    save_top_k=1      )
 if early_stopping_callback:
  extra_callbacks.append(__UpperCamelCase      )
 if logging_callback is None:
  UpperCAmelCase__					:  Tuple					   =					LoggingCallback()

 UpperCAmelCase__					:  Optional[int]					   =					{}

 if args.fpaa:
  UpperCAmelCase__					:  Any					   =					16

 if args.gpus > 1:
  UpperCAmelCase__					:  Any					   =					"""auto"""
  UpperCAmelCase__					:  str					   =					"""ddp"""

 UpperCAmelCase__					:  Dict					   =					args.accumulate_grad_batches
 UpperCAmelCase__					:  Tuple					   =					None
 UpperCAmelCase__					:  Tuple					   =					"""auto"""

 UpperCAmelCase__					:  Any					   =					pl.Trainer.from_argparse_args(
     __UpperCamelCase							,    weights_summary=__UpperCamelCase							,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,    logger=__UpperCamelCase							,    val_check_interval=1							,    num_sanity_val_steps=2							,    **__UpperCamelCase							,    )

 if args.do_train:
  trainer.fit(__UpperCamelCase      )

 else:
  print("""RAG modeling tests with new set functions successfuly executed!"""      )
 return trainer





