

from ..utils import DummyObject, requires_backends


_UpperCamelCase			      =		None







def 		_lowercase			(					*lowercase__       ,     **lowercase__						):
      requires_backends(lowercase__       ,     ['''sentencepiece''', '''tokenizers''']						)





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				,							A_				,							A_=None				,							A_="base"				,							A_=None				,							A_=None				,							A_=None				,							**A_				,							) ->Tuple:



       '''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_ )
       __lowerCAmelCase :						List[str] = 0
       __lowerCAmelCase :						Optional[Any] = Path(self.hparams.output_dir )
       __lowerCAmelCase :						Optional[Any] = self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             __lowerCAmelCase :						Tuple = 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:
             __lowerCAmelCase :						PretrainedConfig = config

       __lowerCAmelCase :						Union[str, Any] = ('''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:
             __lowerCAmelCase :						Tuple = AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,							cache_dir=A_				,							)
       else:
             __lowerCAmelCase :						PreTrainedTokenizer = tokenizer
       __lowerCAmelCase :						Optional[Any] = MODEL_MODES[mode]
       if model is None:
             __lowerCAmelCase :						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:
             __lowerCAmelCase :						Tuple = model
 def      UpperCamelCase__  (							self				,							*A_				,							**A_ ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.model_type.from_pretrained(*A_				,							**A_ )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





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



       '''simple docstring'''





       __lowerCAmelCase :						int = self.model
       __lowerCAmelCase :						int = ['''bias''', '''LayerNorm.weight''']
       __lowerCAmelCase :						Dict = [
           {
               '''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:
             __lowerCAmelCase :						List[Any] = Adafactor(
                 A_				,							lr=self.hparams.learning_rate				,							scale_parameter=A_				,							relative_step=A_ )

       else:
             __lowerCAmelCase :						Union[str, Any] = AdamW(
                 A_				,							lr=self.hparams.learning_rate				,							eps=self.hparams.adam_epsilon )
       __lowerCAmelCase :						str = optimizer

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

       return [optimizer], [scheduler]
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       return self.validation_step(A_				,							A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->List[str]:



       '''simple docstring'''





       return self.validation_end(A_ )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = max(1				,							self.hparams.gpus )  # TODO: consider num_tpu_cores
       __lowerCAmelCase :						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      UpperCamelCase__  (							self				,							A_ ) ->int:



       '''simple docstring'''





       if mode == "test":
             __lowerCAmelCase :						List[Any] = len(self.test_dataloader().dataset )
       else:
             __lowerCAmelCase :						int = self.get_dataloader('''train'''				,							self.hparams.train_batch_size				,							shuffle=A_ )
             __lowerCAmelCase :						str = len(self.train_dataloader().dataset )
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ = False ) ->int:



       '''simple docstring'''





       raise NotImplementedError('''You must implement this for your task''' )
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       return self.train_loader
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       return self.get_dataloader('''dev'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.get_dataloader('''test'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''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      UpperCamelCase__  (							self				,							A_ ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = self.output_dir.joinpath('''best_tfmr''' )
       __lowerCAmelCase :						List[str] = self.step_count
       self.model.save_pretrained(A_ )
       self.tokenizer.save_pretrained(A_ )





 @staticmethod
 def      UpperCamelCase__  (							A_				,							A_ ) ->str:



       '''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=''''''				,							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      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						str = trainer.lr_schedulers[0]['''scheduler''']
       __lowerCAmelCase :						Optional[int] = {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
       pl_module.logger.log_metrics(A_ )
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->List[Any]:



       '''simple docstring'''





       rank_zero_info('''***** Validation results *****''' )
       __lowerCAmelCase :						str = 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      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Tuple:



       '''simple docstring'''





       rank_zero_info('''***** Test results *****''' )
       __lowerCAmelCase :						Dict = trainer.callback_metrics
       # Log and save results to file
       __lowerCAmelCase :						Optional[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 		_lowercase			(					lowercase__       ,     lowercase__						):
      #  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=4_2       ,     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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__=None       ,     lowercase__=True       ,     lowercase__=[]       ,     lowercase__=None       ,     lowercase__=None       ,     **lowercase__       ,     ):
      pl.seed_everything(args.seed						)

      # init model
      __lowerCAmelCase :						str = Path(model.hparams.output_dir						)
      odir.mkdir(exist_ok=lowercase__						)

      # add custom checkpoints
      if checkpoint_callback is None:
            __lowerCAmelCase :						int = 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:
            __lowerCAmelCase :						str = LoggingCallback()

      __lowerCAmelCase :						Dict = {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            __lowerCAmelCase :						List[Any] = 1_6
            __lowerCAmelCase :						Optional[Any] = args.fpaa_opt_level

      if args.gpus > 1:
            __lowerCAmelCase :						str = '''ddp'''

      __lowerCAmelCase :						int = args.accumulate_grad_batches
      __lowerCAmelCase :						Optional[Any] = extra_train_kwargs.get('''accelerator'''       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = extra_train_kwargs.get('''profiler'''       ,     lowercase__						)

      __lowerCAmelCase :						List[str] = pl.Trainer.from_argparse_args(
          lowercase__       ,     weights_summary=lowercase__       ,     callbacks=[logging_callback] + extra_callbacks       ,     logger=lowercase__       ,     checkpoint_callback=lowercase__       ,     **lowercase__       ,     )

      if args.do_train:
            trainer.fit(lowercase__						)

      return trainer


