





import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase_   =   logging.getLogger(__name__)




def 						A_			(  )			->						str:
       _snake_case      :							Dict     				=						ArgumentParser()
       _snake_case      :							int     				=						parser.parse_known_args()
       for arg in unknown:
              if arg.startswith(('''-''', '''--''')  ):
                     parser.add_argument(arg.split('''='''  )[0]  )

       return parser.parse_args()




def 						A_			(  )			->						Tuple:
       _snake_case      :							Dict     				=						parse_args()
       _snake_case      :							List[Any]     				=						8888
       _snake_case      :							Tuple     				=						int(os.environ['''SM_NUM_GPUS''']  )
       _snake_case      :							Union[str, Any]     				=						json.loads(os.environ['''SM_HOSTS''']  )
       _snake_case      :							Tuple     				=						len(lowercase_  )
       _snake_case      :							Optional[Any]     				=						os.environ['''SM_CURRENT_HOST''']
       _snake_case      :							Union[str, Any]     				=						hosts.index(lowercase_  )
       _snake_case      :							Union[str, Any]     				=						'''INFO'''

       if num_nodes > 1:
              _snake_case      :							Dict     				=						f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]  )}'''
       else:
              _snake_case      :							int     				=						f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]  )}'''
       try:
              subprocess.run(lowercase_      ,		shell=lowercase_  )
       except Exception as e:
              logger.info(lowercase_  )


if __name__ == "__main__":
   main()













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


lowerCAmelCase_   =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCAmelCase_   =   {
    "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
lowerCAmelCase_   =   {
    "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
}
lowerCAmelCase_   =   sorted(arg_to_scheduler.keys())
lowerCAmelCase_   =   "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 				A    (pl.LightningModule				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   Optional[int]:



             '''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(lowercase_					)
             _snake_case      :							Union[str, Any]     				=						0
             _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
             _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    _snake_case      :							List[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=lowercase_     ,  **lowercase_     ,  )
             else:
                    _snake_case      :							PretrainedConfig     				=						config

             _snake_case      :							Optional[Any]     				=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
             for p in extra_model_params:
                    if getattr(self.hparams     ,  lowercase_     ,  lowercase_					):
                           assert hasattr(self.config     ,  lowercase_					), F'''model config doesn\'t have a `{p}` attribute'''
                           setattr(self.config     ,  lowercase_     ,  getattr(self.hparams     ,  lowercase_					)					)

             if tokenizer is None:
                    _snake_case      :							List[Any]     				=						AutoTokenizer.from_pretrained(
                        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=lowercase_     ,  )
             else:
                    _snake_case      :							PreTrainedTokenizer     				=						tokenizer
             _snake_case      :							str     				=						MODEL_MODES[mode]
             if model is None:
                    _snake_case      :							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=lowercase_     ,  )
             else:
                    _snake_case      :							Optional[int]     				=						model



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

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



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



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model
             _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
             _snake_case      :							Optional[Any]     				=						[
                 {
                     '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                     '''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:
                    _snake_case      :							Optional[int]     				=						Adafactor(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)

             else:
                    _snake_case      :							Optional[Any]     				=						AdamW(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
             _snake_case      :							Optional[int]     				=						optimizer

             _snake_case      :							Tuple     				=						self.get_lr_scheduler()

             return [optimizer], [scheduler]



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

             return self.validation_step(lowercase_     ,  lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             return self.validation_end(lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
             _snake_case      :							Dict     				=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
             return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



      def    __a (					self     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             if stage == "test":
                    _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
             else:
                    _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:



             '''simple docstring'''

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



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return self.train_loader



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

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



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

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



      @pl.utilities.rank_zero_only
      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
             _snake_case      :							str     				=						self.step_count
             self.model.save_pretrained(lowercase_					)
             self.tokenizer.save_pretrained(lowercase_					)



      @staticmethod
      def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

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


class 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''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 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
             _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
             pl_module.logger.log_metrics(lowercase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

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




def 						A_			(  lowercase_      ,		lowercase_  )			->						None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		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=lowercase_      ,		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=lowercase_  )
       parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		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=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
       parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
       parser.add_argument(
           '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
       pl.seed_everything(args.seed  )

       # init model
       _snake_case      :							int     				=						Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=lowercase_  )

       # add custom checkpoints
       if checkpoint_callback is None:
              _snake_case      :							Union[str, 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(lowercase_  )
       if logging_callback is None:
              _snake_case      :							Optional[int]     				=						LoggingCallback()

       _snake_case      :							Union[str, Any]     				=						{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _snake_case      :							Any     				=						16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
              _snake_case      :							Dict     				=						'''ddp'''

       _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
       _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       _snake_case      :							Tuple     				=						'''auto'''

       _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
           lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)

       if args.do_train:
              trainer.fit(lowercase_  )

       return trainer






