

from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
 from .data.data_collator import DataCollator
 from .modeling_utils import PreTrainedModel
 from .tokenization_utils_base import PreTrainedTokenizerBase
 from .trainer_callback import TrainerCallback
 from .trainer_utils import EvalPrediction, PredictionOutput
 from .training_args import TrainingArguments


_UpperCamelCase			      =		logging.get_logger(__name__)







class   __lowercase				(lowerCAmelCase__			):
 def __init__(							self				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = None				,							A_ = (None, None)				,							A_ = None				,							) ->List[str]:



       '''simple docstring'''





       super().__init__(
           model=UpperCamelCase__				,							args=UpperCamelCase__				,							data_collator=UpperCamelCase__				,							train_dataset=UpperCamelCase__				,							eval_dataset=UpperCamelCase__				,							tokenizer=UpperCamelCase__				,							model_init=UpperCamelCase__				,							compute_metrics=UpperCamelCase__				,							callbacks=UpperCamelCase__				,							optimizers=UpperCamelCase__				,							preprocess_logits_for_metrics=UpperCamelCase__				,							)

       # Override self.model.generation_config if a GenerationConfig is specified in args.
       # Priority: args.generation_config > model.generation_config > default GenerationConfig.
       if self.args.generation_config is not None:
             __lowerCAmelCase :						List[str] = self.load_generation_config(self.args.generation_config )
             __lowerCAmelCase :						Any = gen_config
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->GenerationConfig:



       '''simple docstring'''





       if isinstance(UpperCamelCase__				,							UpperCamelCase__ ):
             return deepcopy(UpperCamelCase__ )

       # str or Path
       __lowerCAmelCase :						int = Path(UpperCamelCase__ ) if isinstance(UpperCamelCase__				,							UpperCamelCase__ ) else gen_config_arg
       __lowerCAmelCase :						Optional[int] = None

       # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
       # This step is required in order to determine config_file_name
       if pretrained_model_name.is_file():
             __lowerCAmelCase :						int = pretrained_model_name.name
             __lowerCAmelCase :						Tuple = pretrained_model_name.parent
       # dir path
       elif pretrained_model_name.is_dir():
             pass
       # model id or URL
       else:
             __lowerCAmelCase :						List[Any] = gen_config_arg

       __lowerCAmelCase :						str = GenerationConfig.from_pretrained(UpperCamelCase__				,							UpperCamelCase__ )
       return gen_config
 def      UpperCamelCase__  (							self				,							A_ = None				,							A_ = None				,							A_ = "eval"				,							**A_				,							) ->Dict[str, float]:



       '''simple docstring'''





       __lowerCAmelCase :						str = gen_kwargs.copy()
       if gen_kwargs.get('''max_length''' ) is None and gen_kwargs.get('''max_new_tokens''' ) is None:
             __lowerCAmelCase :						int = self.args.generation_max_length
       __lowerCAmelCase :						Tuple = (
           gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
       )
       __lowerCAmelCase :						Tuple = gen_kwargs

       return super().evaluate(UpperCamelCase__				,							ignore_keys=UpperCamelCase__				,							metric_key_prefix=UpperCamelCase__ )
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None				,							A_ = "test"				,							**A_				,							) ->"PredictionOutput":



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = gen_kwargs.copy()
       if gen_kwargs.get('''max_length''' ) is None and gen_kwargs.get('''max_new_tokens''' ) is None:
             __lowerCAmelCase :						Optional[int] = self.args.generation_max_length
       __lowerCAmelCase :						Any = (
           gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
       )
       __lowerCAmelCase :						List[str] = gen_kwargs

       return super().predict(UpperCamelCase__				,							ignore_keys=UpperCamelCase__				,							metric_key_prefix=UpperCamelCase__ )
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_ = None				,							**A_				,							) ->Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:



       '''simple docstring'''





       if not self.args.predict_with_generate or prediction_loss_only:
             return super().prediction_step(
                 UpperCamelCase__				,							UpperCamelCase__				,							prediction_loss_only=UpperCamelCase__				,							ignore_keys=UpperCamelCase__ )

       __lowerCAmelCase :						Optional[int] = "labels" in inputs
       __lowerCAmelCase :						Optional[int] = self._prepare_inputs(UpperCamelCase__ )

       # XXX: adapt synced_gpus for fairscale as well
       # Priority (handled in generate):
       # gen_kwargs > model.generation_config > default GenerationConfig()

       if len(UpperCamelCase__ ) == 0 and hasattr(self				,							'''_gen_kwargs''' ):
             __lowerCAmelCase :						Union[str, Any] = self._gen_kwargs.copy()

       if gen_kwargs.get('''max_length''' ) is None and gen_kwargs.get('''max_new_tokens''' ) is None:
             __lowerCAmelCase :						Any = self.model.config.max_length
       __lowerCAmelCase :						Tuple = (
           gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams''' ) is not None else self.model.config.num_beams
       )
       __lowerCAmelCase :						Union[str, Any] = True if is_deepspeed_zeroa_enabled() else False
       __lowerCAmelCase :						Dict = (
           gen_kwargs["synced_gpus"] if gen_kwargs.get('''synced_gpus''' ) is not None else default_synced_gpus
       )

       # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
       # (otherwise, it would continue generating from the padded `decoder_input_ids`)
       if (
           "labels" in inputs
           and "decoder_input_ids" in inputs
           and inputs["labels"].shape == inputs["decoder_input_ids"].shape
       ):
             __lowerCAmelCase :						Dict = {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
       __lowerCAmelCase :						List[Any] = self.model.generate(**UpperCamelCase__				,							**UpperCamelCase__ )

       # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
       # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
       # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
       if self.model.generation_config._from_model_config:
             __lowerCAmelCase :						Dict = False

       # Retrieves GenerationConfig from model.generation_config
       __lowerCAmelCase :						str = self.model.generation_config
       # in case the batch is shorter than max length, the output should be padded
       if generated_tokens.shape[-1] < gen_config.max_length:
             __lowerCAmelCase :						List[str] = self._pad_tensors_to_max_len(UpperCamelCase__				,							gen_config.max_length )
       elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
             __lowerCAmelCase :						Tuple = self._pad_tensors_to_max_len(UpperCamelCase__				,							gen_config.max_new_tokens + 1 )

       with torch.no_grad():
             if has_labels:
                   with self.compute_loss_context_manager():
                         __lowerCAmelCase :						Optional[Any] = model(**UpperCamelCase__ )
                   if self.label_smoother is not None:
                         __lowerCAmelCase :						Dict = self.label_smoother(UpperCamelCase__				,							inputs['''labels'''] ).mean().detach()
                   else:
                         __lowerCAmelCase :						List[str] = (outputs["loss"] if isinstance(UpperCamelCase__				,							UpperCamelCase__ ) else outputs[0]).mean().detach()
             else:
                   __lowerCAmelCase :						Union[str, Any] = None

       if self.args.prediction_loss_only:
             return loss, None, None

       if has_labels:
             __lowerCAmelCase :						Optional[int] = inputs["labels"]
             if labels.shape[-1] < gen_config.max_length:
                   __lowerCAmelCase :						List[Any] = self._pad_tensors_to_max_len(UpperCamelCase__				,							gen_config.max_length )
             elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                   __lowerCAmelCase :						int = self._pad_tensors_to_max_len(UpperCamelCase__				,							gen_config.max_new_tokens + 1 )
       else:
             __lowerCAmelCase :						str = None

       return loss, generated_tokens, labels





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



       '''simple docstring'''





       if self.tokenizer is not None and hasattr(self.tokenizer				,							'''pad_token_id''' ):
             # If PAD token is not defined at least EOS token has to be defined
             __lowerCAmelCase :						Dict = (
                 self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
             )
       else:
             if self.model.config.pad_token_id is not None:
                   __lowerCAmelCase :						Optional[int] = self.model.config.pad_token_id
             else:
                   raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors''' )

       __lowerCAmelCase :						Any = pad_token_id * torch.ones(
           (tensor.shape[0], max_length)				,							dtype=tensor.dtype				,							device=tensor.device )
       __lowerCAmelCase :						Tuple = tensor
       return padded_tensor





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


