
"""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


__UpperCAmelCase       =  logging.getLogger(__name__)




class 			_SCREAMING_SNAKE_CASE					(			a__							):
			UpperCAmelCase_  :List[str]				 =						"""token-classification"""






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






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






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

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






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






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

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

						lowerCAmelCase_    :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__       ),
						}

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






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






			def  __lowerCAmelCase (					self       ,   __A       )			->     Tuple:
						# updating to test_epoch_end instead of deprecated test_end
						lowerCAmelCase_    :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
						lowerCAmelCase_    :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  __lowerCAmelCase (					__A       ,   __A       )			->     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__":
				__UpperCAmelCase       =  argparse.ArgumentParser()
				add_generic_args(parser, os.getcwd())
				__UpperCAmelCase       =  NERTransformer.add_model_specific_args(parser, os.getcwd())
				__UpperCAmelCase       =  parser.parse_args()
				__UpperCAmelCase       =  NERTransformer(args)
				__UpperCAmelCase       =  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
								__UpperCAmelCase       =  sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
								__UpperCAmelCase       =  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()


