




"""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


lowercase__  		=		logging.getLogger(__name__)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """token-classification"""
			def __init__(						self						,   lowercase						):
							if type(lowercase						) == dict:
											_lowerCamelCase  :    Tuple					=					Namespace(**lowercase						)
							_lowerCamelCase  :    str					=					import_module('tasks'						)
							try:
											_lowerCamelCase  :    Optional[Any]					=					getattr(lowercase						,   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  :    Dict					=					self.token_classification_task.get_labels(hparams.labels						)
							_lowerCamelCase  :    List[Any]					=					CrossEntropyLoss().ignore_index
							super().__init__(lowercase						,   len(self.labels						)						,   self.mode						)
			def       A_					(						self						,   **lowercase						):
							return self.model(**lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    int					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
							if self.config.model_type != "distilbert":
											_lowerCamelCase  :    Any					=					(
											    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(**lowercase						)
							_lowerCamelCase  :    Optional[int]					=					outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					self.hparams
							for mode in ["train", "dev", "test"]:
											_lowerCamelCase  :    Any					=					self._feature_file(lowercase						)
											if os.path.exists(lowercase						) and not args.overwrite_cache:
															logger.info('Loading features from cached file %s'						,   lowercase						)
															_lowerCamelCase  :    str					=					torch.load(lowercase						)
											else:
															logger.info('Creating features from dataset file at %s'						,   args.data_dir						)
															_lowerCamelCase  :    Tuple					=					self.token_classification_task.read_examples_from_file(args.data_dir						,   lowercase						)
															_lowerCamelCase  :    Union[str, Any]					=					self.token_classification_task.convert_examples_to_features(
															    lowercase						,   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=lowercase						,   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'						,   lowercase						)
															torch.save(lowercase						,   lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = False						):
							_lowerCamelCase  :    List[Any]					=					self._feature_file(lowercase						)
							logger.info('Loading features from cached file %s'						,   lowercase						)
							_lowerCamelCase  :    Dict					=					torch.load(lowercase						)
							_lowerCamelCase  :    List[Any]					=					torch.tensor([f.input_ids for f in features]						,   dtype=torch.long						)
							_lowerCamelCase  :    Tuple					=					torch.tensor([f.attention_mask for f in features]						,   dtype=torch.long						)
							if features[0].token_type_ids is not None:
											_lowerCamelCase  :    Dict					=					torch.tensor([f.token_type_ids for f in features]						,   dtype=torch.long						)
							else:
											_lowerCamelCase  :    Dict					=					torch.tensor([0 for f in features]						,   dtype=torch.long						)
											# HACK(we will not use this anymore soon)
							_lowerCamelCase  :    List[Any]					=					torch.tensor([f.label_ids for f in features]						,   dtype=torch.long						)
							return DataLoader(
							    TensorDataset(lowercase						,   lowercase						,   lowercase						,   lowercase						)						,   batch_size=lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							"""Compute validation""" ""
							_lowerCamelCase  :    Any					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
							if self.config.model_type != "distilbert":
											_lowerCamelCase  :    Optional[int]					=					(
											    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
											)  # XLM and RoBERTa don"t use token_type_ids
							_lowerCamelCase  :    Union[str, Any]					=					self(**lowercase						)
							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					outputs[:2]
							_lowerCamelCase  :    List[str]					=					logits.detach().cpu().numpy()
							_lowerCamelCase  :    Union[str, Any]					=					inputs['labels'].detach().cpu().numpy()
							return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Optional[Any]					=					torch.stack([x['val_loss'] for x in outputs]						).mean()
							_lowerCamelCase  :    Union[str, Any]					=					np.concatenate([x['pred'] for x in outputs]						,   axis=0						)
							_lowerCamelCase  :    List[Any]					=					np.argmax(lowercase						,   axis=2						)
							_lowerCamelCase  :    Any					=					np.concatenate([x['target'] for x in outputs]						,   axis=0						)

							_lowerCamelCase  :    List[str]					=					dict(enumerate(self.labels						)						)
							_lowerCamelCase  :    Optional[int]					=					[[] for _ in range(out_label_ids.shape[0]						)]
							_lowerCamelCase  :    int					=					[[] 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  :    List[str]					=					{
							    'val_loss': val_loss_mean,
							    'accuracy_score': accuracy_score(lowercase						,   lowercase						),
							    'precision': precision_score(lowercase						,   lowercase						),
							    'recall': recall_score(lowercase						,   lowercase						),
							    'f1': fa_score(lowercase						,   lowercase						),
							}

							_lowerCamelCase  :    Union[str, Any]					=					dict(results.items()						)
							_lowerCamelCase  :    Tuple					=					results
							return ret, preds_list, out_label_list
			def       A_					(						self						,   lowercase						):
							# when stable
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					self._eval_end(lowercase						)
							_lowerCamelCase  :    int					=					ret['log']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
			def       A_					(						self						,   lowercase						):
							# updating to test_epoch_end instead of deprecated test_end
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					self._eval_end(lowercase						)

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							_lowerCamelCase  :    List[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_					(						lowercase						,   lowercase						):
							# Add NER specific options
							BaseTransformer.add_model_specific_args(lowercase						,   lowercase						)
							parser.add_argument(
							    '--task_type'						,   default='NER'						,   type=lowercase						,   help='Task type to fine tune in training (e.g. NER, POS, etc)'						)
							parser.add_argument(
							    '--max_seq_length'						,   default=128						,   type=lowercase						,   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=lowercase						,   help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'						,   )
							parser.add_argument(
							    '--gpus'						,   default=0						,   type=lowercase						,   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__":
			lowercase__  		=		argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			lowercase__  		=		NERTransformer.add_model_specific_args(parser, os.getcwd())
			lowercase__  		=		parser.parse_args()
			lowercase__  		=		NERTransformer(args)
			lowercase__  		=		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
						lowercase__  		=		sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
						lowercase__  		=		model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					int(lowercase__	)
				if decimal in (0, 1):  # Exit cases for the recursion
								return str(lowercase__	)
				_lowerCamelCase,				_lowerCamelCase  :    Dict					=					divmod(lowercase__							,					2	)
				return binary_recursive(lowercase__	) + str(lowercase__	)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					str(lowercase__	).strip()
				if not number:
								raise ValueError('No input value was provided'	)
				_lowerCamelCase  :    str					=					'-' if number.startswith('-'	) else ''
				_lowerCamelCase  :    Union[str, Any]					=					number.lstrip('-'	)
				if not number.isnumeric():
								raise ValueError('Input value is not an integer'	)
				return f'''{negative}0b{binary_recursive(int(lowercase__	)	)}'''


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

			testmod()