






'''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


lowerCamelCase_							      =      logging.getLogger(__name__)

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

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

class      _UpperCAmelCase    (  pl.LightningModule		):




   """simple docstring"""

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





         '''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(__UpperCAmelCase	)
         _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=__UpperCAmelCase   ,   **__UpperCAmelCase   ,   )
         else:
               _A      =			config

         _A      =			("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:
               _A      =			AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,   cache_dir=__UpperCAmelCase   ,   )
         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=__UpperCAmelCase   ,   )
         else:
               _A      =			model

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   *__UpperCAmelCase				:			str   ,   **__UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			self.model_type.from_pretrained(*__UpperCAmelCase   ,   **__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''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				:			List[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(
                   __UpperCAmelCase   ,   lr=self.hparams.learning_rate   ,   scale_parameter=__UpperCAmelCase   ,   relative_step=__UpperCAmelCase	)

         else:
               _A      =			AdamW(
                   __UpperCAmelCase   ,   lr=self.hparams.learning_rate   ,   eps=self.hparams.adam_epsilon	)
         _A      =			optimizer

         _A      =			self.get_lr_scheduler()

         return [optimizer], [scheduler]

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         return self.validation_step(__UpperCAmelCase   ,   __UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         return self.validation_end(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''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				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			bool = False	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         return self.train_loader

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         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 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Dict[str, Any]	):





         '''simple docstring'''





         _A      =			self.output_dir.joinpath("best_tfmr"	)
         _A      =			self.step_count
         self.model.save_pretrained(__UpperCAmelCase	)
         self.tokenizer.save_pretrained(__UpperCAmelCase	)

   @staticmethod
   def 		lowerCAmelCase				(	__UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Union[str, Any]	):





         '''simple docstring'''





         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=32   ,   type=__UpperCAmelCase	)
         parser.add_argument("--eval_batch_size"   ,   default=32   ,   type=__UpperCAmelCase	)
         parser.add_argument("--adafactor"   ,   action="store_true"	)





class      _UpperCAmelCase    (  pl.Callback		):




   """simple docstring"""

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





         '''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(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			pl.Trainer   ,   __UpperCAmelCase				:			pl.LightningModule	):





         '''simple docstring'''





         rank_zero_info("***** Validation results *****"	)
         _A      =			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 		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(__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       __lowercase		(    __lowercase     ,						__lowercase ) ->		None:

      '''simple docstring'''


      parser.add_argument(
          "--output_dir"     ,						default=__lowercase     ,						type=__lowercase     ,						required=__lowercase     ,						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=__lowercase     ,						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=__lowercase )
      parser.add_argument("--max_grad_norm"     ,						dest="gradient_clip_val"     ,						default=1.0     ,						type=__lowercase     ,						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=__lowercase     ,						default=1     ,						help="Number of updates steps to accumulate before performing a backward/update pass."     ,						)
      parser.add_argument("--seed"     ,						type=__lowercase     ,						default=42     ,						help="random seed for initialization" )
      parser.add_argument(
          "--data_dir"     ,						default=__lowercase     ,						type=__lowercase     ,						required=__lowercase     ,						help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."     ,						)


def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=None     ,						__lowercase=True     ,						__lowercase=[]     ,						__lowercase=None     ,						__lowercase=None     ,						**__lowercase     ,						) ->		List[str]:

      '''simple docstring'''


      pl.seed_everything(args.seed )

      # init model
      _A      =			Path(model.hparams.output_dir )
      odir.mkdir(exist_ok=__lowercase )

      # 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(__lowercase )
      if logging_callback is None:
            _A      =			LoggingCallback()

      _A      =			{}

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

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

      _A      =			args.accumulate_grad_batches
      _A      =			extra_train_kwargs.get("accelerator"     ,						__lowercase )
      _A      =			extra_train_kwargs.get("profiler"     ,						__lowercase )

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

      if args.do_train:
            trainer.fit(__lowercase )

      return trainer













'''simple docstring'''




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowerCamelCase_							      =      logging.getLogger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
   snake_case					       =		field(
       default='''NER'''							,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
   snake_case					       =		field(default=snake_case_							,					metadata={'''help''': '''Set this flag to use fast tokenization.'''}		)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,					)





@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
   snake_case					       =		field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
   snake_case					       =		field(
       default=1_28							,					metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }							,					)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    ) ->		Any:

      '''simple docstring'''


      _A      =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
      _A  ,			_A  ,			_A      =			parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir )
          and os.listdir(training_args.output_dir )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                " --overwrite_output_dir to overcome." )

      _A      =			import_module("tasks" )

      try:
            _A      =			getattr(__lowercase     ,						model_args.task_type )
            _A      =			token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,						datefmt="%m/%d/%Y %H:%M:%S"     ,						level=logging.INFO     ,						)
      logger.info(
          "n_replicas: %s, distributed training: %s, 16-bits training: %s"     ,						training_args.n_replicas     ,						bool(training_args.n_replicas > 1 )     ,						training_args.fpaa     ,						)
      logger.info("Training/evaluation parameters %s"     ,						__lowercase )

      # Prepare Token Classification task
      _A      =			token_classification_task.get_labels(data_args.labels )
      _A      =			dict(enumerate(__lowercase ) )
      _A      =			len(__lowercase )

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      _A      =			AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,						num_labels=__lowercase     ,						idalabel=__lowercase     ,						labelaid={label: i for i, label in enumerate(__lowercase )}     ,						cache_dir=model_args.cache_dir     ,						)
      _A      =			AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,						cache_dir=model_args.cache_dir     ,						use_fast=model_args.use_fast     ,						)

      with training_args.strategy.scope():
            _A      =			TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path     ,						from_pt=bool(".bin" in model_args.model_name_or_path )     ,						config=__lowercase     ,						cache_dir=model_args.cache_dir     ,						)

      # Get datasets
      _A      =			(
          TFTokenClassificationDataset(
              token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						model_type=config.model_type     ,						max_seq_length=data_args.max_seq_length     ,						overwrite_cache=data_args.overwrite_cache     ,						mode=Split.train     ,						)
          if training_args.do_train
          else None
      )
      _A      =			(
          TFTokenClassificationDataset(
              token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						model_type=config.model_type     ,						max_seq_length=data_args.max_seq_length     ,						overwrite_cache=data_args.overwrite_cache     ,						mode=Split.dev     ,						)
          if training_args.do_eval
          else None
      )

      def align_predictions(__lowercase     ,						__lowercase ) -> Tuple[List[int], List[int]]:
            _A      =			np.argmax(__lowercase     ,						axis=2 )
            _A  ,			_A      =			preds.shape
            _A      =			[[] for _ in range(__lowercase )]
            _A      =			[[] for _ in range(__lowercase )]

            for i in range(__lowercase ):
                  for j in range(__lowercase ):
                        if label_ids[i, j] != -100:
                              out_label_list[i].append(label_map[label_ids[i][j]] )
                              preds_list[i].append(label_map[preds[i][j]] )

            return preds_list, out_label_list

      def compute_metrics(__lowercase ) -> Dict:
            _A  ,			_A      =			align_predictions(p.predictions     ,						p.label_ids )

            return {
                "precision": precision_score(__lowercase     ,						__lowercase ),
                "recall": recall_score(__lowercase     ,						__lowercase ),
                "f1": fa_score(__lowercase     ,						__lowercase ),
            }

      # Initialize our Trainer
      _A      =			TFTrainer(
          model=__lowercase     ,						args=__lowercase     ,						train_dataset=train_dataset.get_dataset() if train_dataset else None     ,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None     ,						compute_metrics=__lowercase     ,						)

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir )

      # Evaluation
      _A      =			{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            _A      =			trainer.evaluate()
            _A      =			os.path.join(training_args.output_dir     ,						"eval_results.txt" )

            with open(__lowercase     ,						"w" ) as writer:
                  logger.info("***** Eval results *****" )

                  for key, value in result.items():
                        logger.info("  %s = %s"     ,						__lowercase     ,						__lowercase )
                        writer.write("%s = %s\n" % (key, value) )

                  results.update(__lowercase )

    # Predict
      if training_args.do_predict:
            _A      =			TFTokenClassificationDataset(
                token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						model_type=config.model_type     ,						max_seq_length=data_args.max_seq_length     ,						overwrite_cache=data_args.overwrite_cache     ,						mode=Split.test     ,						)

            _A  ,			_A  ,			_A      =			trainer.predict(test_dataset.get_dataset() )
            _A  ,			_A      =			align_predictions(__lowercase     ,						__lowercase )
            _A      =			classification_report(__lowercase     ,						__lowercase )

            logger.info("\n%s"     ,						__lowercase )

            _A      =			os.path.join(training_args.output_dir     ,						"test_results.txt" )

            with open(__lowercase     ,						"w" ) as writer:
                  writer.write("%s\n" % report )

            # Save predictions
            _A      =			os.path.join(training_args.output_dir     ,						"test_predictions.txt" )

            with open(__lowercase     ,						"w" ) as writer:
                  with open(os.path.join(data_args.data_dir     ,						"test.txt" )     ,						"r" ) as f:
                        _A      =			0

                        for line in f:
                              if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
                                    writer.write(__lowercase )

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    _A      =			line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"

                                    writer.write(__lowercase )
                              else:
                                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."     ,						line.split()[0] )

      return results


if __name__ == "__main__":
    main()





