

'''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


lowerCamelCase				:   Tuple      				=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCamelCase				:   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
lowerCamelCase				:   int      				=  {
    "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				:   Any      				=  sorted(arg_to_scheduler.keys())
lowerCamelCase				:   Any      				=  "{" + ", ".join(arg_to_scheduler_choices) + "}"







class 							A__					( pl.LightningModule						):





  def __init__(	self   :			int							,      _a   :			argparse.Namespace							,      _a   :			str=None							,      _a   :			str="base"							,      _a   :			Optional[Any]=None							,      _a   :			Dict=None							,      _a   :			Any=None							,      **_a   :			Dict							,      )			->					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(UpperCamelCase__				)
       _SCREAMING_SNAKE_CASE												=0
       _SCREAMING_SNAKE_CASE												=Path(self.hparams.output_dir				)
       _SCREAMING_SNAKE_CASE												=self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
            _SCREAMING_SNAKE_CASE												=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=UpperCamelCase__							,      **UpperCamelCase__							,      )
       else:
            _SCREAMING_SNAKE_CASE												=config

       _SCREAMING_SNAKE_CASE												=('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
       for p in extra_model_params:
            if getattr(self.hparams							,      UpperCamelCase__							,      UpperCamelCase__				):
                 assert hasattr(self.config							,      UpperCamelCase__				), f"model config doesn\'t have a `{p}` attribute"
                 setattr(self.config							,      UpperCamelCase__							,      getattr(self.hparams							,      UpperCamelCase__				)				)

       if tokenizer is None:
            _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,      cache_dir=UpperCamelCase__							,      )
       else:
            _SCREAMING_SNAKE_CASE												=tokenizer
       _SCREAMING_SNAKE_CASE												=MODEL_MODES[mode]
       if model is None:
            _SCREAMING_SNAKE_CASE												=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=UpperCamelCase__							,      )
       else:
            _SCREAMING_SNAKE_CASE												=model





  def 		A						(	self   :			Optional[Any]							,      *_a   :			Any							,      **_a   :			List[str]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_type.from_pretrained(*UpperCamelCase__							,      **UpperCamelCase__				)





  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






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





  def 		A						(	self   :			List[str]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model
       _SCREAMING_SNAKE_CASE												=['bias', 'LayerNorm.weight']
       _SCREAMING_SNAKE_CASE												=[
           {
               '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:
            _SCREAMING_SNAKE_CASE												=Adafactor(
                UpperCamelCase__							,      lr=self.hparams.learning_rate							,      scale_parameter=UpperCamelCase__							,      relative_step=UpperCamelCase__				)

       else:
            _SCREAMING_SNAKE_CASE												=AdamW(
                UpperCamelCase__							,      lr=self.hparams.learning_rate							,      eps=self.hparams.adam_epsilon				)
       _SCREAMING_SNAKE_CASE												=optimizer

       _SCREAMING_SNAKE_CASE												=self.get_lr_scheduler()

       return [optimizer], [scheduler]





  def 		A						(	self   :			Any							,      _a   :			Any							,      _a   :			Optional[int]				)			->					Dict:





       '''simple docstring'''






       return self.validation_step(UpperCamelCase__							,      UpperCamelCase__				)





  def 		A						(	self   :			Tuple							,      _a   :			Optional[int]				)			->					str:





       '''simple docstring'''






       return self.validation_end(UpperCamelCase__				)





  def 		A						(	self   :			int				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=max(1							,      self.hparams.gpus				)  # TODO: consider num_tpu_cores
       _SCREAMING_SNAKE_CASE												=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   :			List[str]							,      _a   :			Dict				)			->					Any:





       '''simple docstring'''






       if stage == "test":
            _SCREAMING_SNAKE_CASE												=len(self.test_dataloader().dataset				)
       else:
            _SCREAMING_SNAKE_CASE												=self.get_dataloader('train'							,      self.hparams.train_batch_size							,      shuffle=UpperCamelCase__				)
            _SCREAMING_SNAKE_CASE												=len(self.train_dataloader().dataset				)





  def 		A						(	self   :			List[str]							,      _a   :			str							,      _a   :			int							,      _a   :			bool = False				)			->					Tuple:





       '''simple docstring'''






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





  def 		A						(	self   :			List[Any]				)			->					Optional[int]:





       '''simple docstring'''






       return self.train_loader





  def 		A						(	self   :			Union[str, Any]				)			->					Dict:





       '''simple docstring'''






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





  def 		A						(	self   :			Union[str, Any]				)			->					Dict:





       '''simple docstring'''






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





  def 		A						(	self   :			Optional[Any]							,      _a   :			Any				)			->					Any:





       '''simple docstring'''






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





  @pl.utilities.rank_zero_only
  def 		A						(	self   :			Any							,      _a   :			Dict[str, Any]				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.output_dir.joinpath('best_tfmr'				)
       _SCREAMING_SNAKE_CASE												=self.step_count
       self.model.save_pretrained(UpperCamelCase__				)
       self.tokenizer.save_pretrained(UpperCamelCase__				)





  @staticmethod
  def 		A						(	_a   :			int							,      _a   :			Union[str, Any]				)			->					Dict:





       '''simple docstring'''






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







class 							A__					( pl.Callback						):





  def 		A						(	self   :			Dict							,      _a   :			int							,      _a   :			str				)			->					Optional[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   :			Optional[Any]							,      _a   :			Any							,      _a   :			Union[str, Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=trainer.lr_schedulers[0]['scheduler']
       _SCREAMING_SNAKE_CASE												={f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
       pl_module.logger.log_metrics(UpperCamelCase__				)





  def 		A						(	self   :			List[str]							,      _a   :			pl.Trainer							,      _a   :			pl.LightningModule				)			->					Optional[int]:





       '''simple docstring'''






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





  def 		A						(	self   :			int							,      _a   :			pl.Trainer							,      _a   :			pl.LightningModule				)			->					Union[str, Any]:





       '''simple docstring'''






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


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  str       )   ->    List[str]:
     """simple docstring"""




     parser.add_argument(
         '--output_dir'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				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=_UpperCamelCase							,				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=_UpperCamelCase       )
     parser.add_argument('--max_grad_norm'							,				dest='gradient_clip_val'							,				default=1.0							,				type=_UpperCamelCase							,				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=_UpperCamelCase							,				default=1							,				help='Number of updates steps to accumulate before performing a backward/update pass.'							,				)
     parser.add_argument('--seed'							,				type=_UpperCamelCase							,				default=42							,				help='random seed for initialization'       )
     parser.add_argument(
         '--data_dir'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'							,				)







def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  int							,				_UpperCamelCase		:  Tuple=None							,				_UpperCamelCase		:  int=True							,				_UpperCamelCase		:  Optional[int]=None							,				_UpperCamelCase		:  Union[str, Any]=[]							,				_UpperCamelCase		:  Optional[Any]=None							,				_UpperCamelCase		:  Optional[int]=None							,				**_UpperCamelCase		:  str							,				)   ->    str:
     """simple docstring"""




     pl.seed_everything(args.seed       )

     # init model
     _SCREAMING_SNAKE_CASE												=Path(model.hparams.output_dir       )
     odir.mkdir(exist_ok=_UpperCamelCase       )

     # add custom checkpoints
     if checkpoint_callback is None:
          _SCREAMING_SNAKE_CASE												=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(_UpperCamelCase       )
     if logging_callback is None:
          _SCREAMING_SNAKE_CASE												=LoggingCallback()

     _SCREAMING_SNAKE_CASE												={}

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

     if args.gpus > 1:
          _SCREAMING_SNAKE_CASE												='auto'  # "ddp"
          _SCREAMING_SNAKE_CASE												='ddp'

     _SCREAMING_SNAKE_CASE												=args.accumulate_grad_batches
     _SCREAMING_SNAKE_CASE												=None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _SCREAMING_SNAKE_CASE												='auto'

     _SCREAMING_SNAKE_CASE												=pl.Trainer.from_argparse_args(
         _UpperCamelCase							,				weights_summary=_UpperCamelCase							,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,				logger=_UpperCamelCase							,				**_UpperCamelCase							,				)

     if args.do_train:
          trainer.fit(_UpperCamelCase       )

     return trainer



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase				:   str      				=  {"configuration_dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase				:   Optional[int]      				=  [
         "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DinatForImageClassification",
         "DinatModel",
         "DinatPreTrainedModel",
         "DinatBackbone",
     ]

if TYPE_CHECKING:
     from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dinat import (
              DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DinatBackbone,
              DinatForImageClassification,
              DinatModel,
              DinatPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase				:   Optional[Any]      				=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
