

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_	:			Any  	=      logging.getLogger(__name__)



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[int]  							=	'''token-classification'''






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






					def   __A      (			self   ,	**A__      ):
						return self.model(**A__      )






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

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






					def   __A      (			self      ):
						A__							:   int					       =		self.hparams
						for mode in ["train", "dev", "test"]:
							A__							:   Optional[int]					       =		self._feature_file(A__      )
							if os.path.exists(A__      ) and not args.overwrite_cache:
								logger.info("""Loading features from cached file %s"""   ,	A__      )
								A__							:   int					       =		torch.load(A__      )
							else:
								logger.info("""Creating features from dataset file at %s"""   ,	args.data_dir      )
								A__							:   Optional[int]					       =		self.token_classification_task.read_examples_from_file(args.data_dir   ,	A__      )
								A__							:   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   __A      (			self   ,	A__   ,	A__   ,	A__ = False      ):
						A__							:   Dict					       =		self._feature_file(A__      )
						logger.info("""Loading features from cached file %s"""   ,	A__      )
						A__							:   List[Any]					       =		torch.load(A__      )
						A__							:   Union[str, Any]					       =		torch.tensor([f.input_ids for f in features]   ,	dtype=torch.long      )
						A__							:   Optional[Any]					       =		torch.tensor([f.attention_mask for f in features]   ,	dtype=torch.long      )
						if features[0].token_type_ids is not None:
							A__							:   Any					       =		torch.tensor([f.token_type_ids for f in features]   ,	dtype=torch.long      )
						else:
							A__							:   List[str]					       =		torch.tensor([0 for f in features]   ,	dtype=torch.long      )
							# HACK(we will not use this anymore soon)
						A__							:   Optional[Any]					       =		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__      ):
						"""Compute validation""" ""
						A__							:   Union[str, Any]					       =		{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
						if self.config.model_type != "distilbert":
							A__							:   Optional[int]					       =		(
							    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
							)  # XLM and RoBERTa don"t use token_type_ids
						A__							:   Any					       =		self(**A__      )
						A__							:   int					       =		outputs[:2]
						A__							:   Optional[int]					       =		logits.detach().cpu().numpy()
						A__							:   Tuple					       =		inputs["""labels"""].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






					def   __A      (			self   ,	A__      ):
						A__							:   Any					       =		torch.stack([x["""val_loss"""] for x in outputs]      ).mean()
						A__							:   List[Any]					       =		np.concatenate([x["""pred"""] for x in outputs]   ,	axis=0      )
						A__							:   Any					       =		np.argmax(A__   ,	axis=2      )
						A__							:   int					       =		np.concatenate([x["""target"""] for x in outputs]   ,	axis=0      )

						A__							:   Any					       =		dict(enumerate(self.labels      )      )
						A__							:   Dict					       =		[[] for _ in range(out_label_ids.shape[0]      )]
						A__							:   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]]      )

						A__							:   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__      ),
						}

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






					def   __A      (			self   ,	A__      ):
						# when stable
						A__							:   Union[str, Any]					       =		self._eval_end(A__      )
						A__							:   Optional[Any]					       =		ret["""log"""]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					def   __A      (			self   ,	A__      ):
						# updating to test_epoch_end instead of deprecated test_end
						A__							:   List[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
						A__							:   List[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   __A      (			A__   ,	A__      ):
						# 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_	:			List[str]  	=      argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	A_	:			List[str]  	=      NERTransformer.add_model_specific_args(parser, os.getcwd())
	A_	:			Optional[Any]  	=      parser.parse_args()
	A_	:			List[str]  	=      NERTransformer(args)
	A_	:			Dict  	=      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_	:			int  	=      sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
		A_	:			Optional[Any]  	=      model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)






from typing import Any

def 						UpperCamelCase (lowercase_:			list )					->				list[Any]:
	if not input_list:
		return []
	A__							:   Any					       =		[input_list.count(lowercase_ ) for value in input_list]
	A__							:   List[Any]					       =		max(lowercase_ )  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



