


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


UpperCAmelCase__       =							logging.getLogger(__name__)

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

UpperCAmelCase__       =							{
    '''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
UpperCAmelCase__       =							{
    '''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
}
UpperCAmelCase__       =							sorted(arg_to_scheduler.keys())
UpperCAmelCase__       =							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 				lowerCamelCase__       (	pl.LightningModule):





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

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

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





     def 					__A (self		,					*UpperCAmelCase		,					**UpperCAmelCase	)					->       List[str]:
      _lowercase											=self.model_type.from_pretrained(*UpperCAmelCase		,					**UpperCAmelCase	)





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





     def 					__A (self	)					->       Dict:
      _lowercase											=self.model
      _lowercase											=["bias", "LayerNorm.weight"]
      _lowercase											=[
          {
              "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:
       _lowercase											=Adafactor(
           UpperCAmelCase		,					lr=self.hparams.learning_rate		,					scale_parameter=UpperCAmelCase		,					relative_step=UpperCAmelCase	)

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

      _lowercase											=self.get_lr_scheduler()

      return [optimizer], [scheduler]





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       List[str]:
      return self.validation_step(UpperCAmelCase		,					UpperCAmelCase	)





     def 					__A (self		,					UpperCAmelCase	)					->       Tuple:
      return self.validation_end(UpperCAmelCase	)





     def 					__A (self	)					->       int:
      _lowercase											=max(1		,					self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _lowercase											=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		,					UpperCAmelCase	)					->       Optional[Any]:

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





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False	)					->       List[str]:
      raise NotImplementedError('''You must implement this for your task'''	)





     def 					__A (self	)					->       List[Any]:
      return self.train_loader





     def 					__A (self	)					->       Any:
      return self.get_dataloader('''dev'''		,					self.hparams.eval_batch_size		,					shuffle=UpperCAmelCase	)





     def 					__A (self	)					->       Optional[Any]:
      return self.get_dataloader('''test'''		,					self.hparams.eval_batch_size		,					shuffle=UpperCAmelCase	)





     def 					__A (self		,					UpperCAmelCase	)					->       str:
      return os.path.join(
          self.hparams.data_dir		,					'''cached_{}_{}_{}'''.format(
              UpperCAmelCase		,					list(filter(UpperCAmelCase		,					self.hparams.model_name_or_path.split('''/'''	)	)	).pop()		,					str(self.hparams.max_seq_length	)		,					)		,					)





     @pl.utilities.rank_zero_only
     def 					__A (self		,					UpperCAmelCase	)					->       None:
      _lowercase											=self.output_dir.joinpath('''best_tfmr'''	)
      _lowercase											=self.step_count
      self.model.save_pretrained(UpperCAmelCase	)
      self.tokenizer.save_pretrained(UpperCAmelCase	)





     @staticmethod
     def 					__A (UpperCAmelCase		,					UpperCAmelCase	)					->       Optional[Any]:
      parser.add_argument(
          '''--model_name_or_path'''		,					default=UpperCAmelCase		,					type=UpperCAmelCase		,					required=UpperCAmelCase		,					help='''Path to pretrained model or model identifier from huggingface.co/models'''		,					)
      parser.add_argument(
          '''--config_name'''		,					default=''''''		,					type=UpperCAmelCase		,					help='''Pretrained config name or path if not the same as model_name'''	)
      parser.add_argument(
          '''--tokenizer_name'''		,					default=UpperCAmelCase		,					type=UpperCAmelCase		,					help='''Pretrained tokenizer name or path if not the same as model_name'''		,					)
      parser.add_argument(
          '''--cache_dir'''		,					default=''''''		,					type=UpperCAmelCase		,					help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''		,					)
      parser.add_argument(
          '''--encoder_layerdrop'''		,					type=UpperCAmelCase		,					help='''Encoder layer dropout probability (Optional). Goes into model.config'''		,					)
      parser.add_argument(
          '''--decoder_layerdrop'''		,					type=UpperCAmelCase		,					help='''Decoder layer dropout probability (Optional). Goes into model.config'''		,					)
      parser.add_argument(
          '''--dropout'''		,					type=UpperCAmelCase		,					help='''Dropout probability (Optional). Goes into model.config'''		,					)
      parser.add_argument(
          '''--attention_dropout'''		,					type=UpperCAmelCase		,					help='''Attention dropout probability (Optional). Goes into model.config'''		,					)
      parser.add_argument('''--learning_rate'''		,					default=5e-5		,					type=UpperCAmelCase		,					help='''The initial learning rate for Adam.'''	)
      parser.add_argument(
          '''--lr_scheduler'''		,					default='''linear'''		,					choices=UpperCAmelCase		,					metavar=UpperCAmelCase		,					type=UpperCAmelCase		,					help='''Learning rate scheduler'''		,					)
      parser.add_argument('''--weight_decay'''		,					default=0.0		,					type=UpperCAmelCase		,					help='''Weight decay if we apply some.'''	)
      parser.add_argument('''--adam_epsilon'''		,					default=1e-8		,					type=UpperCAmelCase		,					help='''Epsilon for Adam optimizer.'''	)
      parser.add_argument('''--warmup_steps'''		,					default=0		,					type=UpperCAmelCase		,					help='''Linear warmup over warmup_steps.'''	)
      parser.add_argument('''--num_workers'''		,					default=4		,					type=UpperCAmelCase		,					help='''kwarg passed to DataLoader'''	)
      parser.add_argument('''--num_train_epochs'''		,					dest='''max_epochs'''		,					default=3		,					type=UpperCAmelCase	)
      parser.add_argument('''--train_batch_size'''		,					default=3_2		,					type=UpperCAmelCase	)
      parser.add_argument('''--eval_batch_size'''		,					default=3_2		,					type=UpperCAmelCase	)
      parser.add_argument('''--adafactor'''		,					action='''store_true'''	)







class 				lowerCamelCase__       (	pl.Callback):





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





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





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



def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Optional[Any]:







 """simple docstring"""


 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     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case=None					,						__snake_case=True					,						__snake_case=[]					,						__snake_case=None					,						__snake_case=None					,						**__snake_case					,						)					->      List[str]:







 """simple docstring"""


 pl.seed_everything(args.seed						)

 # init model
 _lowercase											=Path(model.hparams.output_dir						)
 odir.mkdir(exist_ok=_a						)

 # add custom checkpoints
 if checkpoint_callback is None:
  _lowercase											=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:
  _lowercase											=LoggingCallback()

 _lowercase											={}

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

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

 _lowercase											=args.accumulate_grad_batches
 _lowercase											=extra_train_kwargs.get('''accelerator'''					,						_a						)
 _lowercase											=extra_train_kwargs.get('''profiler'''					,						_a						)

 _lowercase											=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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''qdqbert'''





     def __init__(self		,					UpperCAmelCase=3_0_5_2_2		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=2		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-12		,					UpperCAmelCase=True		,					UpperCAmelCase=1		,					UpperCAmelCase=0		,					UpperCAmelCase=2		,					**UpperCAmelCase		,					)					->       str:
      super().__init__(pad_token_id=UpperCAmelCase		,					bos_token_id=UpperCAmelCase		,					eos_token_id=UpperCAmelCase		,					**UpperCAmelCase	)

      _lowercase											=vocab_size
      _lowercase											=max_position_embeddings
      _lowercase											=hidden_size
      _lowercase											=num_hidden_layers
      _lowercase											=num_attention_heads
      _lowercase											=intermediate_size
      _lowercase											=hidden_act
      _lowercase											=hidden_dropout_prob
      _lowercase											=attention_probs_dropout_prob
      _lowercase											=initializer_range
      _lowercase											=type_vocab_size
      _lowercase											=layer_norm_eps
      _lowercase											=use_cache

