





'''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


_SCREAMING_SNAKE_CASE			=							logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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

class 				lowerCAmelCase_						(						pl.LightningModule						):




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

     self.save_hyperparameters(_lowerCAmelCase						)
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = Path(self.hparams.output_dir						)
     _lowerCAmelCase		     = self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
         _lowerCAmelCase		     = 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=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     else:
         _lowerCAmelCase		     = config

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

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




 def 							_snake_case				(		self     ,       *_lowerCAmelCase     ,       **_lowerCAmelCase						)      ->							List[Any]:
     _lowerCAmelCase		     = self.model_type.from_pretrained(*_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = arg_to_scheduler[self.hparams.lr_scheduler]
     _lowerCAmelCase		     = get_schedule_func(
         self.opt     ,       num_warmup_steps=self.hparams.warmup_steps     ,       num_training_steps=self.total_steps()						)
     _lowerCAmelCase		     = {"scheduler": scheduler, "interval": "step", "frequency": 1}
     return scheduler




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = self.model
     _lowerCAmelCase		     = ["bias", "LayerNorm.weight"]
     _lowerCAmelCase		     = [
         {
             "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		     = Adafactor(
             _lowerCAmelCase     ,       lr=self.hparams.learning_rate     ,       scale_parameter=_lowerCAmelCase     ,       relative_step=_lowerCAmelCase						)

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

     _lowerCAmelCase		     = self.get_lr_scheduler()

     return [optimizer], [scheduler]




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[int]:
     return self.validation_step(_lowerCAmelCase     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     return self.validation_end(_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = max(1     ,       self.hparams.gpus						)  # TODO: consider num_tpu_cores
     _lowerCAmelCase		     = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[int]:
     if mode == "test":
         _lowerCAmelCase		     = len(self.test_dataloader().dataset						)
     else:
         _lowerCAmelCase		     = self.get_dataloader("train"     ,       self.hparams.train_batch_size     ,       shuffle=_lowerCAmelCase						)
         _lowerCAmelCase		     = len(self.train_dataloader().dataset						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = False						)      ->							Union[str, Any]:
     raise NotImplementedError("You must implement this for your task"						)




 def 							_snake_case				(		self						)      ->							str:
     return self.train_loader




 def 							_snake_case				(		self						)      ->							Any:
     return self.get_dataloader("dev"     ,       self.hparams.eval_batch_size     ,       shuffle=_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return self.get_dataloader("test"     ,       self.hparams.eval_batch_size     ,       shuffle=_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							List[str]:
     return os.path.join(
         self.hparams.data_dir     ,       "cached_{}_{}_{}".format(
             _lowerCAmelCase     ,       list(filter(_lowerCAmelCase     ,       self.hparams.model_name_or_path.split("/"						)						)						).pop()     ,       str(self.hparams.max_seq_length						)     ,       )     ,       )




 @pl.utilities.rank_zero_only
 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = self.output_dir.joinpath("best_tfmr"						)
     _lowerCAmelCase		     = self.step_count
     self.model.save_pretrained(_lowerCAmelCase						)
     self.tokenizer.save_pretrained(_lowerCAmelCase						)





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







class 				lowerCAmelCase_						(						pl.Callback						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Any:
     _lowerCAmelCase		     = trainer.lr_schedulers[0]["scheduler"]
     _lowerCAmelCase		     = {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
     pl_module.logger.log_metrics(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Any:
     rank_zero_info("***** Validation results *****"						)
     _lowerCAmelCase		     = trainer.callback_metrics
     # Log results
     for key in sorted(_lowerCAmelCase						):
         if key not in ["log", "progress_bar"]:
             rank_zero_info("{} = {}\n".format(_lowerCAmelCase     ,       str(metrics[key]						)						)						)





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







def 		__a(SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





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





def 		__a(SCREAMING_SNAKE_CASE_					:   BaseTransformer						,    SCREAMING_SNAKE_CASE_					:   argparse.Namespace						,    SCREAMING_SNAKE_CASE_					:   Tuple=None						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=True						,    SCREAMING_SNAKE_CASE_					:   Tuple=[]						,    SCREAMING_SNAKE_CASE_					:   str=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    **SCREAMING_SNAKE_CASE_					:   List[str]						,    ):



    '''simple docstring'''





    pl.seed_everything(args.seed      )

    # init model
    _lowerCAmelCase		     = Path(model.hparams.output_dir      )
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )

    # add custom checkpoints
    if checkpoint_callback is None:
        _lowerCAmelCase		     = 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(SCREAMING_SNAKE_CASE_      )
    if logging_callback is None:
        _lowerCAmelCase		     = LoggingCallback()

    _lowerCAmelCase		     = {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        _lowerCAmelCase		     = 16
        _lowerCAmelCase		     = args.fpaa_opt_level

    if args.gpus > 1:
        _lowerCAmelCase		     = "ddp"

    _lowerCAmelCase		     = args.accumulate_grad_batches
    _lowerCAmelCase		     = extra_train_kwargs.get("accelerator"						,    SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = extra_train_kwargs.get("profiler"						,    SCREAMING_SNAKE_CASE_      )

    _lowerCAmelCase		     = pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE_						,    weights_summary=SCREAMING_SNAKE_CASE_						,    callbacks=[logging_callback] + extra_callbacks						,    logger=SCREAMING_SNAKE_CASE_						,    checkpoint_callback=SCREAMING_SNAKE_CASE_						,    **SCREAMING_SNAKE_CASE_						,    )

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE_      )

    return trainer









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    _lowerCAmelCase		     = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    _lowerCAmelCase		     = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(SCREAMING_SNAKE_CASE_						,    n + 1      ):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


