

import argparse
import json
import subprocess
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					[]

 UpperCAmelCase__					:  List[str]					   =					(
     F"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
     """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
 )
 UpperCAmelCase__					:  Any					   =					subprocess.run(__UpperCamelCase							,    shell=__UpperCamelCase							,    stdout=subprocess.PIPE      )
 UpperCAmelCase__					:  List[str]					   =					output.stdout.decode("""utf-8"""      )
 UpperCAmelCase__					:  Any					   =					json.loads(__UpperCamelCase      )

 UpperCAmelCase__					:  Dict					   =					status["""runners"""]
 for runner in runners:
  if runner["name"] in target_runners:
   if runner["status"] == "offline":
    offline_runners.append(__UpperCamelCase      )

    # save the result so we can report them on Slack
 with open("""offline_runners.txt"""							,    """w"""      ) as fp:
  fp.write(json.dumps(__UpperCamelCase      )      )

 if len(__UpperCamelCase      ) > 0:
  UpperCAmelCase__					:  str					   =					"""\n""".join([x["""name"""] for x in offline_runners]      )
  raise ValueError(F"The following runners are offline:\n{failed}"      )


if __name__ == "__main__":
      def   lowerCAmelCase						(					__UpperCamelCase      ):
       '''simple docstring'''





       return values.split(""","""      )

      __UpperCAmelCase =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--target_runners',
          default=None,
          type=list_str,
          required=True,
          help='Comma-separated list of runners to check status.',
      )

      parser.add_argument(
          '--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
      )
      __UpperCAmelCase =				parser.parse_args()




      get_runner_status(args.target_runners, args.token)








"""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





