





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							:      List[Any]						=	logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowerCAmelCase							:      int						=	{
    '''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							:      Union[str, Any]						=	{
    '''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							:      Union[str, Any]						=	'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class 					__magic_name__  (							pl.LightningModule							):





     """simple docstring"""



     def __init__(							self       :List[str]			,      snake_case       :argparse.Namespace			,      snake_case       :List[str]=None			,      snake_case       :str="base"			,      snake_case       :Union[str, Any]=None			,      snake_case       :Dict=None			,      snake_case       :Optional[int]=None			,      **snake_case       :int			,      ):





        '''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(snake_case  )
        A_					:				str          =			0
        A_					:				str          =			Path(self.hparams.output_dir  )
        A_					:				List[Any]          =			self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
           A_					:				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=snake_case			,      **snake_case			,      )
        else:
           A_					:				PretrainedConfig          =			config

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

        if tokenizer is None:
           A_					:				Optional[Any]          =			AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,      cache_dir=snake_case			,      )
        else:
           A_					:				PreTrainedTokenizer          =			tokenizer
        A_					:				int          =			MODEL_MODES[mode]
        if model is None:
           A_					:				List[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=snake_case			,      )
        else:
           A_					:				Optional[int]          =			model



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      *snake_case       :Union[str, Any]			,      **snake_case       :int  ):





        '''simple docstring'''




        A_					:				List[str]          =			self.model_type.from_pretrained(*snake_case			,      **snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			arg_to_scheduler[self.hparams.lr_scheduler]
        A_					:				Optional[int]          =			get_schedule_func(
            self.opt			,      num_warmup_steps=self.hparams.warmup_steps			,      num_training_steps=self.total_steps()  )
        A_					:				Dict          =			{"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler



     def 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





        '''simple docstring'''




        A_					:				int          =			self.model
        A_					:				Optional[Any]          =			["bias", "LayerNorm.weight"]
        A_					:				str          =			[
            {
                "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:
           A_					:				Optional[int]          =			Adafactor(
               snake_case			,      lr=self.hparams.learning_rate			,      scale_parameter=snake_case			,      relative_step=snake_case  )

        else:
           A_					:				Any          =			AdamW(
               snake_case			,      lr=self.hparams.learning_rate			,      eps=self.hparams.adam_epsilon  )
        A_					:				str          =			optimizer

        A_					:				str          =			self.get_lr_scheduler()

        return [optimizer], [scheduler]



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :List[Any]			,      snake_case       :Tuple  ):





        '''simple docstring'''




        return self.validation_step(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :List[Any]  ):





        '''simple docstring'''




        return self.validation_end(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :int  ):





        '''simple docstring'''




        if mode == "test":
           A_					:				List[Any]          =			len(self.test_dataloader().dataset  )
        else:
           A_					:				List[str]          =			self.get_dataloader("train"			,      self.hparams.train_batch_size			,      shuffle=snake_case  )
           A_					:				Any          =			len(self.train_dataloader().dataset  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :str			,      snake_case       :int			,      snake_case       :bool = False  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        return self.train_loader



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :List[str]  ):





        '''simple docstring'''




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



     @pl.utilities.rank_zero_only
     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :Dict[str, Any]  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			self.output_dir.joinpath("best_tfmr"  )
        A_					:				List[Any]          =			self.step_count
        self.model.save_pretrained(snake_case  )
        self.tokenizer.save_pretrained(snake_case  )





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[Any]			,      snake_case       :int  ):





        '''simple docstring'''




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




class 					__magic_name__  (							pl.Callback							):





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :Any			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			trainer.lr_schedulers[0]["scheduler"]
        A_					:				Optional[int]          =			{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
        pl_module.logger.log_metrics(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :pl.Trainer			,      snake_case       :pl.LightningModule  ):





        '''simple docstring'''




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





     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :pl.Trainer			,      snake_case       :pl.LightningModule  ):





        '''simple docstring'''




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




def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]	) ->						None:
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       "--output_dir"				,				default=_lowerCAmelCase				,				type=_lowerCAmelCase				,				required=_lowerCAmelCase				,				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=_lowerCAmelCase				,				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=_lowerCAmelCase	)
   parser.add_argument("--max_grad_norm"				,				dest="gradient_clip_val"				,				default=1.0				,				type=_lowerCAmelCase				,				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=_lowerCAmelCase				,				default=1				,				help="Number of updates steps to accumulate before performing a backward/update pass."				,				)
   parser.add_argument("--seed"				,				type=_lowerCAmelCase				,				default=42				,				help="random seed for initialization"	)
   parser.add_argument(
       "--data_dir"				,				default=_lowerCAmelCase				,				type=_lowerCAmelCase				,				required=_lowerCAmelCase				,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,				)





def      __snake_case						(					_lowerCAmelCase : BaseTransformer				,				_lowerCAmelCase : argparse.Namespace				,				_lowerCAmelCase : Tuple=None				,				_lowerCAmelCase : Union[str, Any]=True				,				_lowerCAmelCase : int=[]				,				_lowerCAmelCase : Union[str, Any]=None				,				_lowerCAmelCase : int=None				,				**_lowerCAmelCase : Tuple				,				) ->						int:
   pl.seed_everything(args.seed	)

   # init model
   A_					:				Dict          =			Path(model.hparams.output_dir	)
   odir.mkdir(exist_ok=_lowerCAmelCase	)

   # add custom checkpoints
   if checkpoint_callback is None:
      A_					:				Dict          =			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(_lowerCAmelCase	)
   if logging_callback is None:
      A_					:				Optional[Any]          =			LoggingCallback()

   A_					:				str          =			{}

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

   if args.gpus > 1:
      A_					:				List[str]          =			"ddp"

   A_					:				Optional[Any]          =			args.accumulate_grad_batches
   A_					:				Dict          =			extra_train_kwargs.get("accelerator"				,				_lowerCAmelCase	)
   A_					:				Optional[int]          =			extra_train_kwargs.get("profiler"				,				_lowerCAmelCase	)

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

   if args.do_train:
      trainer.fit(_lowerCAmelCase	)

   return trainer







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
