"""simple docstring"""






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       :			int   	=logging.getLogger(__name__)





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  str				    =      'token-classification'

      def __init__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if type(__lowerCAmelCase		) == dict:
                  lowercase								=      Namespace(**__lowerCAmelCase		)
            lowercase								=      import_module("""tasks"""		)
            try:
                  lowercase								=      getattr(__lowerCAmelCase						,			hparams.task_type		)
                  lowercase								=      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__()}'		)
            lowercase								=      self.token_classification_task.get_labels(hparams.labels		)
            lowercase								=      CrossEntropyLoss().ignore_index
            super().__init__(__lowerCAmelCase						,			len(self.labels		)						,			self.mode		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            return self.model(**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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

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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.hparams
            for mode in ["train", "dev", "test"]:
                  lowercase								=      self._feature_file(__lowerCAmelCase		)
                  if os.path.exists(__lowerCAmelCase		) and not args.overwrite_cache:
                        logger.info("""Loading features from cached file %s"""						,			__lowerCAmelCase		)
                        lowercase								=      torch.load(__lowerCAmelCase		)
                  else:
                        logger.info("""Creating features from dataset file at %s"""						,			args.data_dir		)
                        lowercase								=      self.token_classification_task.read_examples_from_file(args.data_dir						,			__lowerCAmelCase		)
                        lowercase								=      self.token_classification_task.convert_examples_to_features(
                            __lowerCAmelCase						,			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=__lowerCAmelCase						,			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"""						,			__lowerCAmelCase		)
                        torch.save(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = False		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      torch.stack([x["""val_loss"""] for x in outputs]		).mean()
            lowercase								=      np.concatenate([x["""pred"""] for x in outputs]						,			axis=0		)
            lowercase								=      np.argmax(__lowerCAmelCase						,			axis=2		)
            lowercase								=      np.concatenate([x["""target"""] for x in outputs]						,			axis=0		)

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

            lowercase								=      {
                """val_loss""": val_loss_mean,
                """accuracy_score""": accuracy_score(__lowerCAmelCase						,			__lowerCAmelCase		),
                """precision""": precision_score(__lowerCAmelCase						,			__lowerCAmelCase		),
                """recall""": recall_score(__lowerCAmelCase						,			__lowerCAmelCase		),
                """f1""": fa_score(__lowerCAmelCase						,			__lowerCAmelCase		),
            }

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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase				,				lowercase				,				lowercase								=      self._eval_end(__lowerCAmelCase		)

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







            """simple docstring"""



            BaseTransformer.add_model_specific_args(__lowerCAmelCase						,			__lowerCAmelCase		)
            parser.add_argument(
                """--task_type"""						,			default="""NER"""						,			type=__lowerCAmelCase						,			help="""Task type to fine tune in training (e.g. NER, POS, etc)"""		)
            parser.add_argument(
                """--max_seq_length"""						,			default=128						,			type=__lowerCAmelCase						,			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=__lowerCAmelCase						,			help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""						,			)
            parser.add_argument(
                """--gpus"""						,			default=0						,			type=__lowerCAmelCase						,			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       :			Tuple   	=argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       __lowerCAmelCase       :			Optional[Any]   	=NERTransformer.add_model_specific_args(parser, os.getcwd())
       __lowerCAmelCase       :			Optional[Any]   	=parser.parse_args()
       __lowerCAmelCase       :			List[Any]   	=NERTransformer(args)
       __lowerCAmelCase       :			Union[str, 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       :			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)




"""simple docstring"""






from __future__ import annotations


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:str						)					->       set[str]:
      '''simple docstring'''


      lowercase				,				lowercase								=      set(lowerCAmelCase__						), [start]

      while stack:
            lowercase								=      stack.pop()
            explored.add(lowerCAmelCase__						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(lowerCAmelCase__						)
      return explored


__lowerCAmelCase       :			str   	={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, """A"""))



