





"""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


a						: int     	=			logging.getLogger(__name__)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					="""token-classification"""






							def __init__(   self      ,					lowerCAmelCase__      )	-> Dict:
								if type(lowerCAmelCase__      ) == dict:
									a      :		str	=				Namespace(**lowerCAmelCase__      )
								a      :		List[str]	=				import_module("tasks"      )
								try:
									a      :		Optional[Any]	=				getattr(lowerCAmelCase__      ,					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      :		Any	=				self.token_classification_task.get_labels(hparams.labels      )
								a      :		Dict	=				CrossEntropyLoss().ignore_index
								super().__init__(lowerCAmelCase__      ,					len(self.labels      )      ,					self.mode      )






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> Tuple:
								return self.model(**lowerCAmelCase__      )






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

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






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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> int:
								a      :		Optional[Any]	=				torch.stack([x["val_loss"] for x in outputs]      ).mean()
								a      :		Tuple	=				np.concatenate([x["pred"] for x in outputs]      ,					axis=0      )
								a      :		Any	=				np.argmax(lowerCAmelCase__      ,					axis=2      )
								a      :		Any	=				np.concatenate([x["target"] for x in outputs]      ,					axis=0      )

								a      :		List[str]	=				dict(enumerate(self.labels      )      )
								a      :		str	=				[[] 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      :		int	=				{
								    "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__      ),
								}

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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[Any]:
								# when stable
								a,       a,       a      :		Dict	=				self._eval_end(lowerCAmelCase__      )
								a      :		Union[str, Any]	=				ret["log"]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Tuple:
								# updating to test_epoch_end instead of deprecated test_end
								a,       a,       a      :		List[Any]	=				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
								a      :		Optional[Any]	=				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__      )	-> Optional[Any]:
								# Add NER specific options
								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__":
		a						: Tuple     	=			argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		a						: Optional[Any]     	=			NERTransformer.add_model_specific_args(parser, os.getcwd())
		a						: Tuple     	=			parser.parse_args()
		a						: Optional[int]     	=			NERTransformer(args)
		a						: 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
				a						: Any     	=			sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
				a						: Optional[int]     	=			model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)









"""simple docstring"""




a						: Any     	=			{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


a						: List[Any]     	=			{value: key for key, value in encode_dict.items()}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	a      :		int	=				""
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception("encode() accepts only letters of the alphabet and spaces"					)
	return encoded






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	if set(_lowercase					) - {"A", "B", " "} != set():
		raise Exception("decode() accepts only 'A', 'B' and spaces"					)
	a      :		Optional[Any]	=				""
	for word in coded.split():
		while len(_lowercase					) != 0:
			decoded += decode_dict[word[:5]]
			a      :		List[Any]	=				word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()


