'''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__  :						Union[str, Any]					=      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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


class 					_lowercase						(   pl.LightningModule ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_="base"       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      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(lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	0
         UpperCAmelCase__				:					Optional[Any] 					=	Path(self.hparams.output_dir				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
            UpperCAmelCase__				:					List[str] 					=	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__				:					Union[str, Any] 					=	('''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__				:					Tuple 					=	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__				:					Tuple 					=	MODEL_MODES[mode]
         if model is None:
            UpperCAmelCase__				:					List[str] 					=	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__				:					Optional[int] 					=	model




      def 		lowerCAmelCase__       (					self       ,*lowerCamelCase_       ,**lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.model_type.from_pretrained(*lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.model
         UpperCAmelCase__				:					Optional[Any] 					=	['''bias''', '''LayerNorm.weight''']
         UpperCAmelCase__				:					List[Any] 					=	[
             {
                 '''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__				:					Tuple 					=	Adafactor(
                lowerCamelCase_       ,lr=self.hparams.learning_rate       ,scale_parameter=lowerCamelCase_       ,relative_step=lowerCamelCase_				)

         else:
            UpperCAmelCase__				:					Optional[int] 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         return self.validation_step(lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         return self.validation_end(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         if stage == "test":
            UpperCAmelCase__				:					List[str] 					=	len(self.test_dataloader().dataset				)
         else:
            UpperCAmelCase__				:					Optional[int] 					=	self.get_dataloader('''train'''       ,self.hparams.train_batch_size       ,shuffle=lowerCamelCase_				)
            UpperCAmelCase__				:					Optional[Any] 					=	len(self.train_dataloader().dataset				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = False				)      ->      str:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return self.train_loader




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         return self.get_dataloader('''dev'''       ,self.hparams.eval_batch_size       ,shuffle=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         return self.get_dataloader('''test'''       ,self.hparams.eval_batch_size       ,shuffle=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.output_dir.joinpath('''best_tfmr'''				)
         UpperCAmelCase__				:					str 					=	self.step_count
         self.model.save_pretrained(lowerCamelCase_				)
         self.tokenizer.save_pretrained(lowerCamelCase_				)




      @staticmethod
      def 		lowerCAmelCase__       (					lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         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 					_lowercase						(   pl.Callback ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''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 ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



         for name, param in pl_module.model.rag.named_parameters():
            if param.grad is None:
               print(lowerCamelCase_				)




class 					_lowercase						(   pl.Callback ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         rank_zero_info('''***** Validation results *****'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         rank_zero_info('''***** Test results *****'''				)
         UpperCAmelCase__				:					List[Any] 					=	trainer.callback_metrics
         # Log and save results to file
         UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Tuple	,      _A       :					str     ):
   '''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(_A     ).parent / '''test_run''' / '''model_checkpoints'''     )	,      type=_A	,      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=_A	,      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=_A     )
   parser.add_argument('''--max_grad_norm'''	,      dest='''gradient_clip_val'''	,      default=1.0	,      type=_A	,      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=_A	,      default=1	,      help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,      )
   parser.add_argument('''--seed'''	,      type=_A	,      default=42	,      help='''random seed for initialization'''     )
   parser.add_argument(
       '''--data_dir'''	,      default=str(Path(_A     ).parent / '''test_run''' / '''dummy-train-data'''     )	,      type=_A	,      help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,      )







def __UpperCamelCase( _A       :					BaseTransformer	,      _A       :					argparse.Namespace	,      _A       :					Optional[Any]=None	,      _A       :					int=True	,      _A       :					Any=[]	,      _A       :					Optional[Any]=None	,      _A       :					Tuple=None	,      **_A       :					Dict	,      ):
   '''simple docstring'''
   pl.seed_everything(args.seed     )

   # init model
   UpperCAmelCase__				:					Tuple 					=	Path(model.hparams.output_dir     )
   odir.mkdir(exist_ok=_A     )

   # add custom checkpoints
   if checkpoint_callback is None:
      UpperCAmelCase__				:					List[str] 					=	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(_A     )
   if logging_callback is None:
      UpperCAmelCase__				:					Dict 					=	LoggingCallback()

   UpperCAmelCase__				:					Any 					=	{}

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

   if args.gpus > 1:
      UpperCAmelCase__				:					Union[str, Any] 					=	'''auto'''
      UpperCAmelCase__				:					List[Any] 					=	'''ddp'''

   UpperCAmelCase__				:					Any 					=	args.accumulate_grad_batches
   UpperCAmelCase__				:					Dict 					=	None
   UpperCAmelCase__				:					List[str] 					=	'''auto'''

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

   if args.do_train:
      trainer.fit(_A     )

   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


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







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

   for issue in open_issues:
      UpperCAmelCase__				:					int 					=	sorted(issue.get_comments()	,      key=lambda _A     : i.created_at	,      reverse=_A     )
      UpperCAmelCase__				:					int 					=	comments[0] if len(_A     ) > 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()




