





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


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



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :int						   =       'token-classification'





      def __init__(			self	,							A_       ):
       '''simple docstring'''




       if type(A_       ) == dict:
        UpperCamelCase     : Dict	     =       Namespace(**A_       )
       UpperCamelCase     : int	     =       import_module("tasks"       )
       try:
        UpperCamelCase     : Dict	     =       getattr(A_	,							hparams.task_type       )
        UpperCamelCase     : TokenClassificationTask	     =       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     : Union[str, Any]	     =       self.token_classification_task.get_labels(hparams.labels       )
       UpperCamelCase     : List[Any]	     =       CrossEntropyLoss().ignore_index
       super().__init__(A_	,							len(self.labels       )	,							self.mode       )





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       return self.model(**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
        UpperCamelCase     : List[str]	     =       (
            batch[2] if self.config.model_type in ["bert", "xlnet"] else None
        )  # XLM and RoBERTa don"t use token_type_ids

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       self.hparams
       for mode in ["train", "dev", "test"]:
        UpperCamelCase     : Union[str, Any]	     =       self._feature_file(A_       )
        if os.path.exists(A_       ) and not args.overwrite_cache:
         logger.info("Loading features from cached file %s"	,							A_       )
         UpperCamelCase     : int	     =       torch.load(A_       )
        else:
         logger.info("Creating features from dataset file at %s"	,							args.data_dir       )
         UpperCamelCase     : Tuple	     =       self.token_classification_task.read_examples_from_file(args.data_dir	,							A_       )
         UpperCamelCase     : Union[str, Any]	     =       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 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = False       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self._feature_file(A_       )
       logger.info("Loading features from cached file %s"	,							A_       )
       UpperCamelCase     : Tuple	     =       torch.load(A_       )
       UpperCamelCase     : Optional[Any]	     =       torch.tensor([f.input_ids for f in features]	,							dtype=torch.long       )
       UpperCamelCase     : Optional[Any]	     =       torch.tensor([f.attention_mask for f in features]	,							dtype=torch.long       )
       if features[0].token_type_ids is not None:
        UpperCamelCase     : Union[str, Any]	     =       torch.tensor([f.token_type_ids for f in features]	,							dtype=torch.long       )
       else:
        UpperCamelCase     : Optional[Any]	     =       torch.tensor([0 for f in features]	,							dtype=torch.long       )
        # HACK(we will not use this anymore soon)
       UpperCamelCase     : Tuple	     =       torch.tensor([f.label_ids for f in features]	,							dtype=torch.long       )
       return DataLoader(
           TensorDataset(A_	,							A_	,							A_	,							A_       )	,							batch_size=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_       ):
       '''simple docstring'''




       """Compute validation""" ""
       UpperCamelCase     : Any	     =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
        UpperCamelCase     : Any	     =       (
            batch[2] if self.config.model_type in ["bert", "xlnet"] else None
        )  # XLM and RoBERTa don"t use token_type_ids
       UpperCamelCase     : Any	     =       self(**A_       )
       UpperCamelCase     ,   UpperCamelCase     : str	     =       outputs[:2]
       UpperCamelCase     : Optional[int]	     =       logits.detach().cpu().numpy()
       UpperCamelCase     : List[str]	     =       inputs["labels"].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       torch.stack([x["val_loss"] for x in outputs]       ).mean()
       UpperCamelCase     : Dict	     =       np.concatenate([x["pred"] for x in outputs]	,							axis=0       )
       UpperCamelCase     : Any	     =       np.argmax(A_	,							axis=2       )
       UpperCamelCase     : Tuple	     =       np.concatenate([x["target"] for x in outputs]	,							axis=0       )

       UpperCamelCase     : List[Any]	     =       dict(enumerate(self.labels       )       )
       UpperCamelCase     : int	     =       [[] for _ in range(out_label_ids.shape[0]       )]
       UpperCamelCase     : Union[str, Any]	     =       [[] 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     : Optional[Any]	     =       {
           "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     : int	     =       dict(results.items()       )
       UpperCamelCase     : Optional[Any]	     =       results
       return ret, preds_list, out_label_list





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Tuple	     =       self._eval_end(A_       )
       UpperCamelCase     : Tuple	     =       ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Any	     =       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     : str	     =       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 	__UpperCamelCase(			A_	,							A_       ):
       '''simple docstring'''




       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__":
    __lowerCamelCase	:					Optional[Any]			  =					argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    __lowerCamelCase	:					List[Any]			  =					NERTransformer.add_model_specific_args(parser, os.getcwd())
    __lowerCamelCase	:					int			  =					parser.parse_args()
    __lowerCamelCase	:					List[Any]			  =					NERTransformer(args)
    __lowerCamelCase	:					List[Any]			  =					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
        __lowerCamelCase	:					Optional[int]			  =					sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
        __lowerCamelCase	:					Optional[int]			  =					model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)







class  A__       :  # Public class to implement a graph





      def __init__(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       row
       UpperCamelCase     : Any	     =       col
       UpperCamelCase     : Optional[Any]	     =       graph





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       UpperCamelCase     : Dict	     =       [-1, 0, 1, -1, 1, -1, 0, 1]
       UpperCamelCase     : Any	     =       True  # Make those cells visited
       for k in range(8       ):
        if self.is_safe(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       ):
         self.diffs(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       )





      def 	__UpperCamelCase(			self       ):  # And finally, count all islands.
       '''simple docstring'''




       UpperCamelCase     : str	     =       [[False for j in range(self.COL       )] for i in range(self.ROW       )]
       UpperCamelCase     : int	     =       0
       for i in range(self.ROW       ):
        for j in range(self.COL       ):
         if visited[i][j] is False and self.graph[i][j] == 1:
          self.diffs(A_	,							A_	,							A_       )
          count += 1
       return count
