






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


SCREAMING_SNAKE_CASE__		:							List[str] 				=				logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				{
    """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
SCREAMING_SNAKE_CASE__		:							List[str] 				=				{
    """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
}
SCREAMING_SNAKE_CASE__		:							Tuple 				=				sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 				UpperCAmelCase_  (		pl.LightningModule		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None	,						_lowerCAmelCase="base"	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						**_lowerCAmelCase	,						):
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(_lowerCAmelCase		)
          UpperCAmelCase__      :  int				      =			0
          UpperCAmelCase__      :  int				      =			Path(self.hparams.output_dir		)
          UpperCAmelCase__      :  Dict				      =			self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               UpperCAmelCase__      :  Optional[int]				      =			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=_lowerCAmelCase	,						**_lowerCAmelCase	,						)
          else:
               UpperCAmelCase__      :  PretrainedConfig				      =			config

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

          if tokenizer is None:
               UpperCAmelCase__      :  str				      =			AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,						cache_dir=_lowerCAmelCase	,						)
          else:
               UpperCAmelCase__      :  PreTrainedTokenizer				      =			tokenizer
          UpperCAmelCase__      :  List[str]				      =			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=_lowerCAmelCase	,						)
          else:
               UpperCAmelCase__      :  Tuple				      =			model




     def   __UpperCAmelCase  (				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			self.model_type.from_pretrained(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			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__      :  Union[str, Any]				      =			{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
          return scheduler




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			self.model
          UpperCAmelCase__      :  int				      =			["""bias""", """LayerNorm.weight"""]
          UpperCAmelCase__      :  int				      =			[
              {
                  """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__      :  Dict				      =			Adafactor(
                   _lowerCAmelCase	,						lr=self.hparams.learning_rate	,						scale_parameter=_lowerCAmelCase	,						relative_step=_lowerCAmelCase		)

          else:
               UpperCAmelCase__      :  Union[str, Any]				      =			AdamW(
                   _lowerCAmelCase	,						lr=self.hparams.learning_rate	,						eps=self.hparams.adam_epsilon		)
          UpperCAmelCase__      :  Optional[int]				      =			optimizer

          UpperCAmelCase__      :  int				      =			self.get_lr_scheduler()

          return [optimizer], [scheduler]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return self.validation_step(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return self.validation_end(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			max(1	,						self.hparams.gpus		)  # TODO: consider num_tpu_cores
          UpperCAmelCase__      :  List[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   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if stage == "test":
               UpperCAmelCase__      :  int				      =			len(self.test_dataloader().dataset		)
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			self.get_dataloader("""train"""	,						self.hparams.train_batch_size	,						shuffle=_lowerCAmelCase		)
               UpperCAmelCase__      :  Optional[int]				      =			len(self.train_dataloader().dataset		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = False		):
          raise NotImplementedError("""You must implement this for your task"""		)




     def   __UpperCAmelCase  (				self		):
          return self.train_loader




     def   __UpperCAmelCase  (				self		):
          return self.get_dataloader("""dev"""	,						self.hparams.eval_batch_size	,						shuffle=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          return self.get_dataloader("""test"""	,						self.hparams.eval_batch_size	,						shuffle=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return os.path.join(
              self.hparams.data_dir	,						"""cached_{}_{}_{}""".format(
                  _lowerCAmelCase	,						list(filter(_lowerCAmelCase	,						self.hparams.model_name_or_path.split("""/"""		)		)		).pop()	,						str(self.hparams.max_seq_length		)	,						)	,						)




     @pl.utilities.rank_zero_only
     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[int]				      =			self.output_dir.joinpath("""best_tfmr"""		)
          UpperCAmelCase__      :  List[str]				      =			self.step_count
          self.model.save_pretrained(_lowerCAmelCase		)
          self.tokenizer.save_pretrained(_lowerCAmelCase		)




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







class 				UpperCAmelCase_  (		pl.Callback		):




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          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 				UpperCAmelCase_  (		pl.Callback		):




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          # print(pl_module.model.rag)
          for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                    print(_lowerCAmelCase		)







class 				UpperCAmelCase_  (		pl.Callback		):




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			trainer.lr_schedulers[0]["""scheduler"""]
          UpperCAmelCase__      :  Optional[int]				      =			{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
          pl_module.logger.log_metrics(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          rank_zero_info("""***** Validation results *****"""		)
          UpperCAmelCase__      :  Tuple				      =			trainer.callback_metrics
          # Log results
          for key in sorted(_lowerCAmelCase		):
               if key not in ["log", "progress_bar"]:
                    rank_zero_info("""{} = {}\n""".format(_lowerCAmelCase	,						str(metrics[key]		)		)		)




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





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			None:

     '''simple docstring'''
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         """--output_dir""" ,							default=str(Path(__lowerCamelCase       ).parent / """test_run""" / """model_checkpoints"""       ) ,							type=__lowerCamelCase ,							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=__lowerCamelCase ,							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=__lowerCamelCase       )
     parser.add_argument("""--max_grad_norm""" ,							dest="""gradient_clip_val""" ,							default=1.0 ,							type=__lowerCamelCase ,							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=__lowerCamelCase ,							default=1 ,							help="""Number of updates steps to accumulate before performing a backward/update pass.""" ,							)
     parser.add_argument("""--seed""" ,							type=__lowerCamelCase ,							default=42 ,							help="""random seed for initialization"""       )
     parser.add_argument(
         """--data_dir""" ,							default=str(Path(__lowerCamelCase       ).parent / """test_run""" / """dummy-train-data"""       ) ,							type=__lowerCamelCase ,							help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" ,							)







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							__lowerCamelCase=True ,							__lowerCamelCase=[] ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							**__lowerCamelCase ,							) ->			Dict:

     '''simple docstring'''
     pl.seed_everything(args.seed       )

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

     # 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(__lowerCamelCase       )
     if logging_callback is None:
          UpperCAmelCase__      :  Optional[Any]				      =			LoggingCallback()

     UpperCAmelCase__      :  List[str]				      =			{}

     if args.fpaa:
          UpperCAmelCase__      :  List[Any]				      =			16

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

     UpperCAmelCase__      :  Optional[Any]				      =			args.accumulate_grad_batches
     UpperCAmelCase__      :  List[Any]				      =			None
     UpperCAmelCase__      :  List[str]				      =			"""auto"""

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

     if args.do_train:
          trainer.fit(__lowerCamelCase       )

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








"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


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




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[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()    )
    ):
      # 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()
