






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

require_version("pytorch_lightning>=1.0.4")

__a					:				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
__a					:				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					:				Any      =      sorted(arg_to_scheduler.keys())
__a					:				Dict      =      "{" + ", ".join(arg_to_scheduler_choices) + "}"


class 					__lowercase					(		pl.LightningModule     ):







     '''simple docstring'''




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


         """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(lowercase_ )
         __A   			=		0
         __A   			=		Path(self.hparams.output_dir )
         __A   			=		self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
             __A   			=		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=lowercase_       ,		**lowercase_       ,		)
         else:
             __A   			=		config

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

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




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		*UpperCamelCase_				:	Optional[Any]       ,		**UpperCamelCase_				:	Union[str, Any] ):


         """simple docstring"""



         __A   			=		self.model_type.from_pretrained(*lowercase_       ,		**lowercase_ )




     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         __A   			=		self.model
         __A   			=		["""bias""", """LayerNorm.weight"""]
         __A   			=		[
             {
                 """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   			=		Adafactor(
                 lowercase_       ,		lr=self.hparams.learning_rate       ,		scale_parameter=lowercase_       ,		relative_step=lowercase_ )

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

         __A   			=		self.get_lr_scheduler()

         return [optimizer], [scheduler]




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	Any       ,		UpperCamelCase_				:	List[Any] ):


         """simple docstring"""



         return self.validation_step(lowercase_       ,		lowercase_ )




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



         return self.validation_end(lowercase_ )




     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         __A   			=		max(1       ,		self.hparams.gpus )  # TODO: consider num_tpu_cores
         __A   			=		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				:	Union[str, Any]       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	int       ,		UpperCamelCase_				:	str       ,		UpperCamelCase_				:	int       ,		UpperCamelCase_				:	bool = False ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         return self.train_loader




     def 						lowerCAmelCase_   ( self				:	Optional[int] ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Union[str, Any]       ,		UpperCamelCase_				:	Any ):


         """simple docstring"""



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




     @pl.utilities.rank_zero_only
     def 						lowerCAmelCase_   ( self				:	Optional[int]       ,		UpperCamelCase_				:	Dict[str, Any] ):


         """simple docstring"""



         __A   			=		self.output_dir.joinpath("""best_tfmr""" )
         __A   			=		self.step_count
         self.model.save_pretrained(lowercase_ )
         self.tokenizer.save_pretrained(lowercase_ )





     @staticmethod
     def 						lowerCAmelCase_   ( UpperCamelCase_				:	List[str]       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



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


class 					__lowercase					(		pl.Callback     ):







     '''simple docstring'''




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	Optional[Any]       ,		UpperCamelCase_				:	Optional[int] ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	pl.Trainer       ,		UpperCamelCase_				:	pl.LightningModule ):


         """simple docstring"""



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





     def 						lowerCAmelCase_   ( self				:	Any       ,		UpperCamelCase_				:	pl.Trainer       ,		UpperCamelCase_				:	pl.LightningModule ):


         """simple docstring"""



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



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Union[str, Any]	, __lowercase      :	Any			)  ->					None:





    """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=4_2	, 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 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Dict	, __lowercase      :	Tuple	, __lowercase      :	Any=None	, __lowercase      :	Dict=True	, __lowercase      :	Any=[]	, __lowercase      :	Any=None	, __lowercase      :	int=None	, **__lowercase      :	Any	, )  ->					Tuple:





    """simple docstring"""

    pl.seed_everything(args.seed			)

    # init model
    __A   			=		Path(model.hparams.output_dir			)
    odir.mkdir(exist_ok=a__			)

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

    __A   			=		{}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        __A   			=		1_6
        __A   			=		args.fpaa_opt_level

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

    __A   			=		args.accumulate_grad_batches
    __A   			=		extra_train_kwargs.get("""accelerator"""	, a__			)
    __A   			=		extra_train_kwargs.get("""profiler"""	, a__			)

    __A   			=		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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase__   = {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = ['''ErnieMTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = [
       '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ErnieMForMultipleChoice''',
       '''ErnieMForQuestionAnswering''',
       '''ErnieMForSequenceClassification''',
       '''ErnieMForTokenClassification''',
       '''ErnieMModel''',
       '''ErnieMPreTrainedModel''',
       '''ErnieMForInformationExtraction''',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__   = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




