


import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_A			=						logging.getLogger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "token-classification"






    def __init__(      self ,       A_   )  ->	Tuple:
         if type(A_   ) == dict:
              __UpperCamelCase   							=Namespace(**A_   )
         __UpperCamelCase   							=import_module('tasks'   )
         try:
              __UpperCamelCase   							=getattr(A_ ,       hparams.task_type   )
              __UpperCamelCase   							=token_classification_task_clazz()
         except AttributeError:
              raise ValueError(
                  f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
                  f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'   )
         __UpperCamelCase   							=self.token_classification_task.get_labels(hparams.labels   )
         __UpperCamelCase   							=CrossEntropyLoss().ignore_index
         super().__init__(A_ ,       len(self.labels   ) ,       self.mode   )






    def _a (      self ,       **A_   )  ->	Union[str, Any]:
         return self.model(**A_   )






    def _a (      self ,       A_ ,       A_   )  ->	Dict:
         __UpperCamelCase   							={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
              __UpperCamelCase   							=(
                  batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
              )  # XLM and RoBERTa don"t use token_type_ids

         __UpperCamelCase   							=self(**A_   )
         __UpperCamelCase   							=outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=self.hparams
         for mode in ["train", "dev", "test"]:
              __UpperCamelCase   							=self._feature_file(A_   )
              if os.path.exists(A_   ) and not args.overwrite_cache:
                   logger.info('Loading features from cached file %s' ,       A_   )
                   __UpperCamelCase   							=torch.load(A_   )
              else:
                   logger.info('Creating features from dataset file at %s' ,       args.data_dir   )
                   __UpperCamelCase   							=self.token_classification_task.read_examples_from_file(args.data_dir ,       A_   )
                   __UpperCamelCase   							=self.token_classification_task.convert_examples_to_features(
                       A_ ,       self.labels ,       args.max_seq_length ,       self.tokenizer ,       cls_token_at_end=bool(self.config.model_type in ['xlnet']   ) ,       cls_token=self.tokenizer.cls_token ,       cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0 ,       sep_token=self.tokenizer.sep_token ,       sep_token_extra=A_ ,       pad_on_left=bool(self.config.model_type in ['xlnet']   ) ,       pad_token=self.tokenizer.pad_token_id ,       pad_token_segment_id=self.tokenizer.pad_token_type_id ,       pad_token_label_id=self.pad_token_label_id ,       )
                   logger.info('Saving features into cached file %s' ,       A_   )
                   torch.save(A_ ,       A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = False   )  ->	DataLoader:
         __UpperCamelCase   							=self._feature_file(A_   )
         logger.info('Loading features from cached file %s' ,       A_   )
         __UpperCamelCase   							=torch.load(A_   )
         __UpperCamelCase   							=torch.tensor([f.input_ids for f in features] ,       dtype=torch.long   )
         __UpperCamelCase   							=torch.tensor([f.attention_mask for f in features] ,       dtype=torch.long   )
         if features[0].token_type_ids is not None:
              __UpperCamelCase   							=torch.tensor([f.token_type_ids for f in features] ,       dtype=torch.long   )
         else:
              __UpperCamelCase   							=torch.tensor([0 for f in features] ,       dtype=torch.long   )
              # HACK(we will not use this anymore soon)
         __UpperCamelCase   							=torch.tensor([f.label_ids for f in features] ,       dtype=torch.long   )
         return DataLoader(
             TensorDataset(A_ ,       A_ ,       A_ ,       A_   ) ,       batch_size=A_   )






    def _a (      self ,       A_ ,       A_   )  ->	int:
         """Compute validation""" ""
         __UpperCamelCase   							={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
              __UpperCamelCase   							=(
                  batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
              )  # XLM and RoBERTa don"t use token_type_ids
         __UpperCamelCase   							=self(**A_   )
         __UpperCamelCase							,				__UpperCamelCase   							=outputs[:2]
         __UpperCamelCase   							=logits.detach().cpu().numpy()
         __UpperCamelCase   							=inputs['labels'].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






    def _a (      self ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=torch.stack([x['val_loss'] for x in outputs]   ).mean()
         __UpperCamelCase   							=np.concatenate([x['pred'] for x in outputs] ,       axis=0   )
         __UpperCamelCase   							=np.argmax(A_ ,       axis=2   )
         __UpperCamelCase   							=np.concatenate([x['target'] for x in outputs] ,       axis=0   )

         __UpperCamelCase   							=dict(enumerate(self.labels   )   )
         __UpperCamelCase   							=[[] for _ in range(out_label_ids.shape[0]   )]
         __UpperCamelCase   							=[[] for _ in range(out_label_ids.shape[0]   )]

         for i in range(out_label_ids.shape[0]   ):
              for j in range(out_label_ids.shape[1]   ):
                   if out_label_ids[i, j] != self.pad_token_label_id:
                        out_label_list[i].append(label_map[out_label_ids[i][j]]   )
                        preds_list[i].append(label_map[preds[i][j]]   )

         __UpperCamelCase   							={
             'val_loss': val_loss_mean,
             'accuracy_score': accuracy_score(A_ ,       A_   ),
             'precision': precision_score(A_ ,       A_   ),
             'recall': recall_score(A_ ,       A_   ),
             'f1': fa_score(A_ ,       A_   ),
         }

         __UpperCamelCase   							=dict(results.items()   )
         __UpperCamelCase   							=results
         return ret, preds_list, out_label_list






    def _a (      self ,       A_   )  ->	List[Any]:
         # when stable
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self._eval_end(A_   )
         __UpperCamelCase   							=ret['log']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






    def _a (      self ,       A_   )  ->	Tuple:
         # updating to test_epoch_end instead of deprecated test_end
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self._eval_end(A_   )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         __UpperCamelCase   							=ret['log']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






    @staticmethod
    def _a (      A_ ,       A_   )  ->	Tuple:
         # Add NER specific options
         BaseTransformer.add_model_specific_args(A_ ,       A_   )
         parser.add_argument(
             '--task_type' ,       default='NER' ,       type=A_ ,       help='Task type to fine tune in training (e.g. NER, POS, etc)'   )
         parser.add_argument(
             '--max_seq_length' ,       default=128 ,       type=A_ ,       help=(
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             ) ,       )

         parser.add_argument(
             '--labels' ,       default='' ,       type=A_ ,       help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.' ,       )
         parser.add_argument(
             '--gpus' ,       default=0 ,       type=A_ ,       help='The number of GPUs allocated for this, it is by default 0 meaning none' ,       )

         parser.add_argument(
             '--overwrite_cache' ,       action='store_true' ,       help='Overwrite the cached training and evaluation sets'   )

         return parser


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   _A			=						NERTransformer.add_model_specific_args(parser, os.getcwd())
   _A			=						parser.parse_args()
   _A			=						NERTransformer(args)
   _A			=						generic_train(model, args)

   if args.do_predict:
      # See https://github.com/huggingface/transformers/issues/3159
      # pl use this default format to create a checkpoint:
      # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
      # /pytorch_lightning/callbacks/model_checkpoint.py#L322
      _A			=						sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
      _A			=						model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)









import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_A			=						'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
     require_version(deps[pkg]      ,					SCREAMING_SNAKE_CASE__    )





