






"""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							=  logging.getLogger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"token-classification"

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

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

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

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

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											__magic_name__:	Optional[Any]							=       self.hparams
											for mode in ["train", "dev", "test"]:
																	__magic_name__:	Union[str, Any]							=       self._feature_file(__snake_case					)
																	if os.path.exists(__snake_case					) and not args.overwrite_cache:
																							logger.info("""Loading features from cached file %s"""     ,		__snake_case					)
																							__magic_name__:	Union[str, Any]							=       torch.load(__snake_case					)
																	else:
																							logger.info("""Creating features from dataset file at %s"""     ,		args.data_dir					)
																							__magic_name__:	str							=       self.token_classification_task.read_examples_from_file(args.data_dir     ,		__snake_case					)
																							__magic_name__:	List[str]							=       self.token_classification_task.convert_examples_to_features(
																							    __snake_case     ,		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=__snake_case     ,		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"""     ,		__snake_case					)
																							torch.save(__snake_case     ,		__snake_case					)

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

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

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      str					)			->							List[Any]:
											__magic_name__:	Union[str, Any]							=       torch.stack([x["""val_loss"""] for x in outputs]					).mean()
											__magic_name__:	Optional[int]							=       np.concatenate([x["""pred"""] for x in outputs]     ,		axis=0					)
											__magic_name__:	Union[str, Any]							=       np.argmax(__snake_case     ,		axis=2					)
											__magic_name__:	Dict							=       np.concatenate([x["""target"""] for x in outputs]     ,		axis=0					)

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

											__magic_name__:	List[Any]							=       {
											    """val_loss""": val_loss_mean,
											    """accuracy_score""": accuracy_score(__snake_case     ,		__snake_case					),
											    """precision""": precision_score(__snake_case     ,		__snake_case					),
											    """recall""": recall_score(__snake_case     ,		__snake_case					),
											    """f1""": fa_score(__snake_case     ,		__snake_case					),
											}

											__magic_name__:	Dict							=       dict(results.items()					)
											__magic_name__:	List[str]							=       results
											return ret, preds_list, out_label_list

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      List[str]					)			->							Any:
											# when stable
											__magic_name__,			__magic_name__,			__magic_name__:	List[Any]							=       self._eval_end(__snake_case					)
											__magic_name__:	Optional[int]							=       ret["""log"""]
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Optional[Any]					)			->							int:
											# updating to test_epoch_end instead of deprecated test_end
											__magic_name__,			__magic_name__,			__magic_name__:	Any							=       self._eval_end(__snake_case					)

											# Converting to the dict required by pl
											# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
											# pytorch_lightning/trainer/logging.py#L139
											__magic_name__:	Dict							=       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   lowerCamelCase__   (			__snake_case  :      Optional[int]     ,		__snake_case  :      List[Any]					)			->							str:
											# Add NER specific options
											BaseTransformer.add_model_specific_args(__snake_case     ,		__snake_case					)
											parser.add_argument(
											    """--task_type"""     ,		default="""NER"""     ,		type=__snake_case     ,		help="""Task type to fine tune in training (e.g. NER, POS, etc)"""					)
											parser.add_argument(
											    """--max_seq_length"""     ,		default=1_2_8     ,		type=__snake_case     ,		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=__snake_case     ,		help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""     ,		)
											parser.add_argument(
											    """--gpus"""     ,		default=0     ,		type=__snake_case     ,		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)











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 1_0_0  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Any							=       0
						for i in range(1      ,   n + 1  ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(f'''{solution() = }''')



