
"""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


lowercase_						    =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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



class 		UpperCAmelCase_	(pl.LightningModule						):
 """simple docstring"""




 def __init__( self       : Union[str, Any]     ,   a_       : argparse.Namespace     ,   a_       : Tuple=None     ,   a_       : Optional[Any]="base"     ,   a_       : Tuple=None     ,   a_       : Optional[Any]=None     ,   a_       : Union[str, Any]=None     ,   **a_       : int     ,   )->					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_ )
      UpperCAmelCase_						:    Dict    					=  0
      UpperCAmelCase_						:    Union[str, Any]    					=  Path(self.hparams.output_dir )
      UpperCAmelCase_						:    List[str]    					=  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=a_     ,   **a_     ,   )
      else:
           UpperCAmelCase_						:    PretrainedConfig    					=  config

      UpperCAmelCase_						:    Tuple    					=  ("""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:
           UpperCAmelCase_						:    Tuple    					=  AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,   cache_dir=a_     ,   )
      else:
           UpperCAmelCase_						:    PreTrainedTokenizer    					=  tokenizer
      UpperCAmelCase_						:    str    					=  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=a_     ,   )
      else:
           UpperCAmelCase_						:    List[Any]    					=  model




 def   a      ( self       : Dict     ,   *a_       : Dict     ,   **a_       : List[str] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.model_type.from_pretrained(*a_     ,   **a_ )




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



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  arg_to_scheduler[self.hparams.lr_scheduler]
      UpperCAmelCase_						:    Dict    					=  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   a      ( self       : int )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.model
      UpperCAmelCase_						:    List[Any]    					=  ["""bias""", """LayerNorm.weight"""]
      UpperCAmelCase_						:    int    					=  [
          {
              """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:
           UpperCAmelCase_						:    Dict    					=  Adafactor(
               a_     ,   lr=self.hparams.learning_rate     ,   scale_parameter=a_     ,   relative_step=a_ )

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

      UpperCAmelCase_						:    Tuple    					=  self.get_lr_scheduler()

      return [optimizer], [scheduler]




 def   a      ( self       : List[Any]     ,   a_       : str     ,   a_       : List[str] )->					List[str]:



      """simple docstring"""

      return self.validation_step(a_     ,   a_ )




 def   a      ( self       : int     ,   a_       : Dict )->					int:



      """simple docstring"""

      return self.validation_end(a_ )




 def   a      ( self       : Any )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  max(1     ,   self.hparams.gpus )  # TODO: consider num_tpu_cores
      UpperCAmelCase_						:    Union[str, 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   a      ( self       : List[Any]     ,   a_       : Any )->					Dict:



      """simple docstring"""

      if stage == "test":
           UpperCAmelCase_						:    Tuple    					=  len(self.test_dataloader().dataset )
      else:
           UpperCAmelCase_						:    List[Any]    					=  self.get_dataloader("""train"""     ,   self.hparams.train_batch_size     ,   shuffle=a_ )
           UpperCAmelCase_						:    Dict    					=  len(self.train_dataloader().dataset )




 def   a      ( self       : List[Any]     ,   a_       : str     ,   a_       : int     ,   a_       : bool = False )->					Dict:



      """simple docstring"""

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




 def   a      ( self       : str )->					int:



      """simple docstring"""

      return self.train_loader




 def   a      ( self       : int )->					Dict:



      """simple docstring"""

      return self.get_dataloader("""dev"""     ,   self.hparams.eval_batch_size     ,   shuffle=a_ )




 def   a      ( self       : Tuple )->					str:



      """simple docstring"""

      return self.get_dataloader("""test"""     ,   self.hparams.eval_batch_size     ,   shuffle=a_ )




 def   a      ( self       : List[Any]     ,   a_       : Optional[Any] )->					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   a      ( self       : str     ,   a_       : Dict[str, Any] )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.output_dir.joinpath("""best_tfmr""" )
      UpperCAmelCase_						:    Any    					=  self.step_count
      self.model.save_pretrained(a_ )
      self.tokenizer.save_pretrained(a_ )






 @staticmethod
 def   a      ( a_       : List[str]     ,   a_       : Union[str, Any] )->					Dict:



      """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 		UpperCAmelCase_	(pl.Callback						):
 """simple docstring"""




 def   a      ( self       : Tuple     ,   a_       : List[str]     ,   a_       : Tuple )->					List[str]:



      """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 		UpperCAmelCase_	(pl.Callback						):
 """simple docstring"""




 def   a      ( self       : int     ,   a_       : Optional[int]     ,   a_       : List[str] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  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(a_ )




 def   a      ( self       : str     ,   a_       : pl.Trainer     ,   a_       : pl.LightningModule )->					Any:



      """simple docstring"""

      rank_zero_info("""***** Validation results *****""" )
      UpperCAmelCase_						:    Union[str, Any]    					=  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   a      ( self       : str     ,   a_       : pl.Trainer     ,   a_       : pl.LightningModule )->					Dict:



      """simple docstring"""

      rank_zero_info("""***** Test results *****""" )
      UpperCAmelCase_						:    Dict    					=  trainer.callback_metrics
      # Log and save results to file
      UpperCAmelCase_						:    List[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 			A_			(					lowercase						, lowercase      )     ->							None:


     """simple docstring"""







     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						, )     ->							int:


     """simple docstring"""







     pl.seed_everything(args.seed      )

     # init model
     UpperCAmelCase_						:    Any    					=  Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=lowercase      )

     # add custom checkpoints
     if checkpoint_callback is None:
          UpperCAmelCase_						:    Optional[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:
          UpperCAmelCase_						:    Any    					=  LoggingCallback()

     UpperCAmelCase_						:    List[Any]    					=  {}

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

     if args.gpus > 1:
          UpperCAmelCase_						:    Any    					=  """auto"""  # "ddp"
          UpperCAmelCase_						:    Optional[Any]    					=  """ddp"""

     UpperCAmelCase_						:    Optional[int]    					=  args.accumulate_grad_batches
     UpperCAmelCase_						:    Dict    					=  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     UpperCAmelCase_						:    str    					=  """auto"""

     UpperCAmelCase_						:    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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_						    =					{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_						    =					[
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       lowercase_						    =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

