





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


a_    :				Tuple					=			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_    :				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
a_    :				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
}
a_    :				int					=			sorted(arg_to_scheduler.keys())
a_    :				List[Any]					=			'{' + ', '.join(arg_to_scheduler_choices) + '}'



class  SCREAMING_SNAKE_CASE_  ( pl.LightningModule			):


  """simple docstring"""





  def __init__(  self	,  A	,  A=None	,  A="base"	,  A=None	,  A=None	,  A=None	,  **A	,  )		->   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(A						)
       __magic_name__				=       0
       __magic_name__				=       Path(self.hparams.output_dir						)
       __magic_name__				=       self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
            __magic_name__				=       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:
            __magic_name__				=       config

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





  def 			__A      (  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.model_type.from_pretrained(*A	,  **A						)





  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       arg_to_scheduler[self.hparams.lr_scheduler]
       __magic_name__				=       get_schedule_func(
           self.opt	,  num_warmup_steps=self.hparams.warmup_steps	,  num_training_steps=self.total_steps()						)
       __magic_name__				=       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
       return scheduler





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__				=       self.model
       __magic_name__				=       ['''bias''', '''LayerNorm.weight''']
       __magic_name__				=       [
           {
               '''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:
            __magic_name__				=       Adafactor(
                A	,  lr=self.hparams.learning_rate	,  scale_parameter=A	,  relative_step=A						)

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

       __magic_name__				=       self.get_lr_scheduler()

       return [optimizer], [scheduler]





  def 			__A      (  self	,  A	,  A						)		->   Optional[int]:





       '''simple docstring'''







       return self.validation_step(A	,  A						)





  def 			__A      (  self	,  A						)		->   str:





       '''simple docstring'''







       return self.validation_end(A						)





  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       __magic_name__				=       max(1	,  self.hparams.gpus						)  # TODO: consider num_tpu_cores
       __magic_name__				=       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						)		->   str:





       '''simple docstring'''







       if mode == "test":
            __magic_name__				=       len(self.test_dataloader().dataset						)
       else:
            __magic_name__				=       self.get_dataloader('''train'''	,  self.hparams.train_batch_size	,  shuffle=A						)
            __magic_name__				=       len(self.train_dataloader().dataset						)





  def 			__A      (  self	,  A	,  A	,  A = False						)		->   Dict:





       '''simple docstring'''







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





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       return self.train_loader





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







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





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







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





  def 			__A      (  self	,  A						)		->   List[Any]:





       '''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						)		->   None:





       '''simple docstring'''







       __magic_name__				=       self.output_dir.joinpath('''best_tfmr'''						)
       __magic_name__				=       self.step_count
       self.model.save_pretrained(A						)
       self.tokenizer.save_pretrained(A						)







  @staticmethod
  def 			__A      (  A	,  A						)		->   Optional[Any]:





       '''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  SCREAMING_SNAKE_CASE_  ( pl.Callback			):


  """simple docstring"""





  def 			__A      (  self	,  A	,  A						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       trainer.lr_schedulers[0]['''scheduler''']
       __magic_name__				=       {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						)		->   Union[str, Any]:





       '''simple docstring'''







       rank_zero_info('''***** Validation results *****'''						)
       __magic_name__				=       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						)		->   str:





       '''simple docstring'''







       rank_zero_info('''***** Test results *****'''						)
       __magic_name__				=       trainer.callback_metrics
       # Log and save results to file
       __magic_name__				=       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   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[int] ,     snake_case_		:						Any						):
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         '''--output_dir''' ,     default=snake_case_ ,     type=snake_case_ ,     required=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=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=snake_case_						)
     parser.add_argument('''--max_grad_norm''' ,     dest='''gradient_clip_val''' ,     default=1.0 ,     type=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=snake_case_ ,     default=1 ,     help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,     )
     parser.add_argument('''--seed''' ,     type=snake_case_ ,     default=42 ,     help='''random seed for initialization'''						)
     parser.add_argument(
         '''--data_dir''' ,     default=snake_case_ ,     type=snake_case_ ,     required=snake_case_ ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' ,     )




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						BaseTransformer ,     snake_case_		:						argparse.Namespace ,     snake_case_		:						List[str]=None ,     snake_case_		:						str=True ,     snake_case_		:						Any=[] ,     snake_case_		:						List[str]=None ,     snake_case_		:						Optional[int]=None ,     **snake_case_		:						Union[str, Any] ,     ):
     pl.seed_everything(args.seed						)

     # init model
     __magic_name__				=       Path(model.hparams.output_dir						)
     odir.mkdir(exist_ok=snake_case_						)

     # add custom checkpoints
     if checkpoint_callback is None:
          __magic_name__				=       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(snake_case_						)
     if logging_callback is None:
          __magic_name__				=       LoggingCallback()

     __magic_name__				=       {}

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

     if args.gpus > 1:
          __magic_name__				=       '''ddp'''

     __magic_name__				=       args.accumulate_grad_batches
     __magic_name__				=       extra_train_kwargs.get('''accelerator''' ,     snake_case_						)
     __magic_name__				=       extra_train_kwargs.get('''profiler''' ,     snake_case_						)

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

     if args.do_train:
          trainer.fit(snake_case_						)

     return trainer






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       0  # Number of params in the encoder

     print('''name'''.ljust(60 ,     ''' '''						) ,     '''Remaining Weights %''' ,     '''Remaining Weight'''						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (mask > 0.0).sum().item()
               else:
                    raise ValueError('''Unknown pruning method'''						)
               remaining_count += mask_ones
               print(name.ljust(60 ,     ''' '''						) ,     str(round(100 * mask_ones / param.numel() ,     3						)						).ljust(20 ,     ''' '''						) ,     str(snake_case_						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''''''						)
     print('''Remaining Weights (global) %: ''' ,     100 * remaining_count / encoder_count						)


if __name__ == "__main__":
    a_    :				Optional[Any]					=			argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
            'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
            'Not needed for `l0`'
        ),
    )
    parser.add_argument(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)