



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         =			logging.getLogger(__name__)


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""token-classification"""



       def __init__(     self     ,lowercase):

             """simple docstring"""





             if type(lowercase) == dict:
                   UpperCAmelCase_  :	List[str]				      =		Namespace(**lowercase)
             UpperCAmelCase_  :	List[Any]				      =		import_module("tasks")
             try:
                   UpperCAmelCase_  :	Dict				      =		getattr(lowercase     ,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_  :	List[Any]				      =		self.token_classification_task.get_labels(hparams.labels)
             UpperCAmelCase_  :	Dict				      =		CrossEntropyLoss().ignore_index
             super().__init__(lowercase     ,len(self.labels)     ,self.mode)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return self.model(**lowercase)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





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

             UpperCAmelCase_  :	Optional[int]				      =		self(**lowercase)
             UpperCAmelCase_  :	Dict				      =		outputs[0]
             # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
             return {"loss": loss}



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.hparams
             for mode in ["train", "dev", "test"]:
                   UpperCAmelCase_  :	Tuple				      =		self._feature_file(lowercase)
                   if os.path.exists(lowercase) and not args.overwrite_cache:
                         logger.info("Loading features from cached file %s"     ,lowercase)
                         UpperCAmelCase_  :	str				      =		torch.load(lowercase)
                   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     ,lowercase)
                         UpperCAmelCase_  :	List[str]				      =		self.token_classification_task.convert_examples_to_features(
                             lowercase     ,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=lowercase     ,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"     ,lowercase)
                         torch.save(lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = False):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self._feature_file(lowercase)
             logger.info("Loading features from cached file %s"     ,lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.load(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		torch.tensor([f.input_ids for f in features]     ,dtype=torch.long)
             UpperCAmelCase_  :	Any				      =		torch.tensor([f.attention_mask for f in features]     ,dtype=torch.long)
             if features[0].token_type_ids is not None:
                   UpperCAmelCase_  :	List[Any]				      =		torch.tensor([f.token_type_ids for f in features]     ,dtype=torch.long)
             else:
                   UpperCAmelCase_  :	Union[str, 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(lowercase     ,lowercase     ,lowercase     ,lowercase)     ,batch_size=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase):

             """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_  :	Optional[Any]				      =		np.argmax(lowercase     ,axis=2)
             UpperCAmelCase_  :	List[str]				      =		np.concatenate([x["target"] for x in outputs]     ,axis=0)

             UpperCAmelCase_  :	Optional[int]				      =		dict(enumerate(self.labels))
             UpperCAmelCase_  :	Union[str, Any]				      =		[[] for _ in range(out_label_ids.shape[0])]
             UpperCAmelCase_  :	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_  :	List[Any]				      =		{
                 "val_loss": val_loss_mean,
                 "accuracy_score": accuracy_score(lowercase     ,lowercase),
                 "precision": precision_score(lowercase     ,lowercase),
                 "recall": recall_score(lowercase     ,lowercase),
                 "f1": fa_score(lowercase     ,lowercase),
             }

             UpperCAmelCase_  :	int				      =		dict(results.items())
             UpperCAmelCase_  :	Optional[Any]				      =		results
             return ret, preds_list, out_label_list



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[Any]				      =		self._eval_end(lowercase)
             UpperCAmelCase_  :	int				      =		ret["log"]
             return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		self._eval_end(lowercase)

             # Converting to the dict required by pl
             # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
             # pytorch_lightning/trainer/logging.py#L139
             UpperCAmelCase_  :	int				      =		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_   (     lowercase     ,lowercase):

             """simple docstring"""





             BaseTransformer.add_model_specific_args(lowercase     ,lowercase)
             parser.add_argument(
                 "--task_type"     ,default="NER"     ,type=lowercase     ,help="Task type to fine tune in training (e.g. NER, POS, etc)")
             parser.add_argument(
                 "--max_seq_length"     ,default=128     ,type=lowercase     ,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=lowercase     ,help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."     ,)
             parser.add_argument(
                 "--gpus"     ,default=0     ,type=lowercase     ,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         =			argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   __lowerCamelCase         =			NERTransformer.add_model_specific_args(parser, os.getcwd())
   __lowerCamelCase         =			parser.parse_args()
   __lowerCamelCase         =			NERTransformer(args)
   __lowerCamelCase         =			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         =			sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
      __lowerCamelCase         =			model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
