



"""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				:     Optional[int]								=  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowerCamelCase				:     str								=  {
    """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				:     List[str]								=  sorted(arg_to_scheduler.keys())
lowerCamelCase				:     Optional[int]								=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class    __snake_case(					pl.LightningModule       ):




    def __init__(   self    ,							A_    ,							A_=None    ,							A_="base"    ,							A_=None    ,							A_=None    ,							A_=None    ,							**A_    ,							):





      '''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_			)
      _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=A_    ,							**A_    ,							)
      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    ,							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:
        _SCREAMING_SNAKE_CASE        =			AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,							cache_dir=A_    ,							)
      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=A_    ,							)
      else:
        _SCREAMING_SNAKE_CASE        =			model




    def   A   (   self    ,							*A_    ,							**A_			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''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			):





      '''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(
            A_    ,							lr=self.hparams.learning_rate    ,							scale_parameter=A_    ,							relative_step=A_			)

      else:
        _SCREAMING_SNAKE_CASE        =			AdamW(
            A_    ,							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    ,							A_    ,							A_			):





      '''simple docstring'''



      return self.validation_step(A_    ,							A_			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      return self.validation_end(A_			)




    def   A   (   self			):





      '''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    ,							A_			):





      '''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=A_			)
        _SCREAMING_SNAKE_CASE        =			len(self.train_dataloader().dataset			)




    def   A   (   self    ,							A_    ,							A_    ,							A_ = False			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      return self.train_loader




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_			):





      '''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    ,							A_			):





      '''simple docstring'''



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




    @staticmethod
    def   A   (   A_    ,							A_			):





      '''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    __snake_case(					pl.Callback       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''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    __snake_case(					pl.Callback       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''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(A_			)




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      rank_zero_info('''***** Validation results *****'''			)
      _SCREAMING_SNAKE_CASE        =			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    ,							A_    ,							A_			):





      '''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(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__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=True       ,							UpperCamelCase__=None       ,							UpperCamelCase__=[]       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							**UpperCamelCase__       ,							):


  '''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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

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