




'''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


UpperCAmelCase_				:     str     			=						logging.getLogger(__name__)

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

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





class 			lowerCAmelCase      (       pl.LightningModule):


       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			,     )					->     Any:



            '''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(__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     0
            __snake_case			    =     Path(self.hparams.output_dir     )
            __snake_case			    =     self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __snake_case			    =     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:
                 __snake_case			    =     config

            __snake_case			    =     ('''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:
                 __snake_case			    =     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:
                 __snake_case			    =     tokenizer
            __snake_case			    =     MODEL_MODES[mode]
            if model is None:
                 __snake_case			    =     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:
                 __snake_case			    =     model


       def 					lowerCAmelCase					(      self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     Optional[Any]:



            '''simple docstring'''




            __snake_case			    =     self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




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

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

            __snake_case			    =     self.get_lr_scheduler()

            return [optimizer], [scheduler]


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Tuple:



            '''simple docstring'''




            return self.validation_step(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            return self.validation_end(__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     List[str]:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False     )					->     Optional[Any]:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self     )					->     Optional[int]:



            '''simple docstring'''




            return self.train_loader


       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     None:



            '''simple docstring'''




            __snake_case			    =     self.output_dir.joinpath('''best_tfmr'''     )
            __snake_case			    =     self.step_count
            self.model.save_pretrained(__SCREAMING_SNAKE_CASE     )
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE     )




       @staticmethod
       def 					lowerCAmelCase					(      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            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 			lowerCAmelCase      (       pl.Callback):


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     trainer.lr_schedulers[0]['''scheduler''']
            __snake_case			    =     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
            pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[str]:



            '''simple docstring'''




            rank_zero_info('''***** Validation results *****'''     )
            __snake_case			    =     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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     str:



            '''simple docstring'''




            rank_zero_info('''***** Test results *****'''     )
            __snake_case			    =     trainer.callback_metrics
            # Log and save results to file
            __snake_case			    =     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 	_UpperCamelCase       (_lowerCamelCase							:   Tuple      ,		_lowerCamelCase							:   str		)->      None:




     '''simple docstring'''





     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 	_UpperCamelCase       (_lowerCamelCase							:   BaseTransformer      ,		_lowerCamelCase							:   argparse.Namespace      ,		_lowerCamelCase							:   Dict=None      ,		_lowerCamelCase							:   Optional[int]=True      ,		_lowerCamelCase							:   List[str]=[]      ,		_lowerCamelCase							:   List[str]=None      ,		_lowerCamelCase							:   Optional[int]=None      ,		**_lowerCamelCase							:   str      ,		)->      Any:




     '''simple docstring'''





     pl.seed_everything(args.seed		)

     # init model
     __snake_case			    =     Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=_lowerCamelCase		)

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

     __snake_case			    =     {}

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

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

     __snake_case			    =     args.accumulate_grad_batches
     __snake_case			    =     extra_train_kwargs.get('''accelerator'''      ,		_lowerCamelCase		)
     __snake_case			    =     extra_train_kwargs.get('''profiler'''      ,		_lowerCamelCase		)

     __snake_case			    =     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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_				:     Dict     			=						{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     List[Any]     			=						['''MarkupLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     int     			=						[
         '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MarkupLMForQuestionAnswering''',
         '''MarkupLMForSequenceClassification''',
         '''MarkupLMForTokenClassification''',
         '''MarkupLMModel''',
         '''MarkupLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
     from .feature_extraction_markuplm import MarkupLMFeatureExtractor
     from .processing_markuplm import MarkupLMProcessor
     from .tokenization_markuplm import MarkupLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_markuplm_fast import MarkupLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_markuplm import (
              MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarkupLMForQuestionAnswering,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMModel,
              MarkupLMPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_				:     Dict     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)






