






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


__snake_case							 =  logging.getLogger(__name__)

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

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







class       UpperCAmelCase_						( pl.LightningModule			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_="base"   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_=None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				List[str]:
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       0
          UpperCamelCase	:List[str]     							=       Path(self.hparams.output_dir			)
          UpperCamelCase	:str     							=       self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
              UpperCamelCase	:Any     							=       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=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_   ,    )
          else:
              UpperCamelCase	:PretrainedConfig     							=       config

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

          if tokenizer is None:
              UpperCamelCase	:str     							=       AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,    cache_dir=SCREAMING_SNAKE_CASE_   ,    )
          else:
              UpperCamelCase	:PreTrainedTokenizer     							=       tokenizer
          UpperCamelCase	:Dict     							=       MODEL_MODES[mode]
          if model is None:
              UpperCamelCase	:Optional[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=SCREAMING_SNAKE_CASE_   ,    )
          else:
              UpperCamelCase	:str     							=       model
      def    UpperCAmelCase						(    self   ,    *SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)    ->				List[str]:
          UpperCamelCase	:Tuple     							=       self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:str     							=       arg_to_scheduler[self.hparams.lr_scheduler]
          UpperCamelCase	:List[str]     							=       get_schedule_func(
              self.opt   ,    num_warmup_steps=self.hparams.warmup_steps   ,    num_training_steps=self.total_steps()			)
          UpperCamelCase	:int     							=       {"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:int     							=       self.model
          UpperCamelCase	:Optional[Any]     							=       ["bias", "LayerNorm.weight"]
          UpperCamelCase	:List[Any]     							=       [
              {
                  "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	:int     							=       Adafactor(
                  SCREAMING_SNAKE_CASE_   ,    lr=self.hparams.learning_rate   ,    scale_parameter=SCREAMING_SNAKE_CASE_   ,    relative_step=SCREAMING_SNAKE_CASE_			)

          else:
              UpperCamelCase	:Dict     							=       AdamW(
                  SCREAMING_SNAKE_CASE_   ,    lr=self.hparams.learning_rate   ,    eps=self.hparams.adam_epsilon			)
          UpperCamelCase	:Dict     							=       optimizer

          UpperCamelCase	:str     							=       self.get_lr_scheduler()

          return [optimizer], [scheduler]
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				List[str]:
          return self.validation_step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				Tuple:
          return self.validation_end(SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:Optional[Any]     							=       max(1   ,    self.hparams.gpus			)  # TODO: consider num_tpu_cores
          UpperCamelCase	:str     							=       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   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:

          if mode == "test":
              UpperCamelCase	:List[Any]     							=       len(self.test_dataloader().dataset			)
          else:
              UpperCamelCase	:Optional[int]     							=       self.get_dataloader('''train'''   ,    self.hparams.train_batch_size   ,    shuffle=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Tuple     							=       len(self.train_dataloader().dataset			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = False			)    ->				List[str]:
          raise NotImplementedError('''You must implement this for your task'''			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          return self.train_loader
      def    UpperCAmelCase						(    self			)    ->				Any:
          return self.get_dataloader('''dev'''   ,    self.hparams.eval_batch_size   ,    shuffle=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          return self.get_dataloader('''test'''   ,    self.hparams.eval_batch_size   ,    shuffle=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				str:
          return os.path.join(
              self.hparams.data_dir   ,    '''cached_{}_{}_{}'''.format(
                  SCREAMING_SNAKE_CASE_   ,    list(filter(SCREAMING_SNAKE_CASE_   ,    self.hparams.model_name_or_path.split('''/'''			)			)			).pop()   ,    str(self.hparams.max_seq_length			)   ,    )   ,    )
      @pl.utilities.rank_zero_only
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				None:
          UpperCamelCase	:List[str]     							=       self.output_dir.joinpath('''best_tfmr'''			)
          UpperCamelCase	:int     							=       self.step_count
          self.model.save_pretrained(SCREAMING_SNAKE_CASE_			)
          self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_			)




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







class       UpperCAmelCase_						( pl.Callback			):



      """simple docstring"""
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				List[Any]:
          UpperCamelCase	:Tuple     							=       trainer.lr_schedulers[0]["scheduler"]
          UpperCamelCase	:List[Any]     							=       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
          pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				str:
          rank_zero_info('''***** Validation results *****'''			)
          UpperCamelCase	:Optional[Any]     							=       trainer.callback_metrics
          # Log results
          for key in sorted(SCREAMING_SNAKE_CASE_			):
              if key not in ["log", "progress_bar"]:
                  rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_   ,    str(metrics[key]			)			)			)




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Tuple:
          rank_zero_info('''***** Test results *****'''			)
          UpperCamelCase	:Optional[Any]     							=       trainer.callback_metrics
          # Log and save results to file
          UpperCamelCase	:int     							=       os.path.join(pl_module.hparams.output_dir   ,    '''test_results.txt'''			)
          with open(SCREAMING_SNAKE_CASE_   ,    '''w'''			) as writer:
              for key in sorted(SCREAMING_SNAKE_CASE_			):
                  if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_   ,    str(metrics[key]			)			)			)
                      writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_   ,    str(metrics[key]			)			)			)







def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     Tuple	):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''					,  default=_a					,  type=_a					,  required=_a					,  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=_a					,  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=_a	)
    parser.add_argument('''--max_grad_norm'''					,  dest='''gradient_clip_val'''					,  default=1.0					,  type=_a					,  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=_a					,  default=1					,  help='''Number of updates steps to accumulate before performing a backward/update pass.'''					,  )
    parser.add_argument('''--seed'''					,  type=_a					,  default=42					,  help='''random seed for initialization'''	)
    parser.add_argument(
        '''--data_dir'''					,  default=_a					,  type=_a					,  required=_a					,  help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''					,  )


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=None					,  SCREAMING_SNAKE_CASE__  :     List[str]=True					,  SCREAMING_SNAKE_CASE__  :     Any=[]					,  SCREAMING_SNAKE_CASE__  :     Optional[int]=None					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]=None					,  **SCREAMING_SNAKE_CASE__  :     str					,  ):
    pl.seed_everything(args.seed	)

    # init model
    UpperCamelCase	:Any     							=       Path(model.hparams.output_dir	)
    odir.mkdir(exist_ok=_a	)

    # add custom checkpoints
    if checkpoint_callback is None:
        UpperCamelCase	:List[str]     							=       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(_a	)
    if logging_callback is None:
        UpperCamelCase	:str     							=       LoggingCallback()

    UpperCamelCase	:Any     							=       {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        UpperCamelCase	:List[str]     							=       16
        UpperCamelCase	:Optional[Any]     							=       args.fpaa_opt_level

    if args.gpus > 1:
        UpperCamelCase	:Optional[Any]     							=       "ddp"

    UpperCamelCase	:List[str]     							=       args.accumulate_grad_batches
    UpperCamelCase	:Dict     							=       extra_train_kwargs.get('''accelerator'''					,  _a	)
    UpperCamelCase	:List[str]     							=       extra_train_kwargs.get('''profiler'''					,  _a	)

    UpperCamelCase	:List[Any]     							=       pl.Trainer.from_argparse_args(
        _a					,  weights_summary=_a					,  callbacks=[logging_callback] + extra_callbacks					,  logger=_a					,  checkpoint_callback=_a					,  **_a					,  )

    if args.do_train:
        trainer.fit(_a	)

    return trainer













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





