






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			:		List[Any]   				=    logging.getLogger(__name__)
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Union[str, Any]		      =    'token-classification'




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   Any	):







											if type(lowercase_	) == dict:
															lowerCAmelCase__      			=						Namespace(**lowercase_	)
											lowerCAmelCase__      			=						import_module('''tasks'''	)
											try:
															lowerCAmelCase__      			=						getattr(lowercase_ ,							hparams.task_type	)
															lowerCAmelCase__      			=						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__      			=						self.token_classification_task.get_labels(hparams.labels	)
											lowerCAmelCase__      			=						CrossEntropyLoss().ignore_index
											super().__init__(lowercase_ ,							len(self.labels	) ,							self.mode	)




							def 						__snake_case					(   self     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											return self.model(**lowercase_	)




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[str]	):
											lowerCAmelCase__      			=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
											if self.config.model_type != "distilbert":
															lowerCAmelCase__      			=						(
															    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
															)  # XLM and RoBERTa don"t use token_type_ids

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




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						self.hparams
											for mode in ["train", "dev", "test"]:
															lowerCAmelCase__      			=						self._feature_file(lowercase_	)
															if os.path.exists(lowercase_	) and not args.overwrite_cache:
																			logger.info('''Loading features from cached file %s''' ,							lowercase_	)
																			lowerCAmelCase__      			=						torch.load(lowercase_	)
															else:
																			logger.info('''Creating features from dataset file at %s''' ,							args.data_dir	)
																			lowerCAmelCase__      			=						self.token_classification_task.read_examples_from_file(args.data_dir ,							lowercase_	)
																			lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   bool = False	):
											lowerCAmelCase__      			=						self._feature_file(lowercase_	)
											logger.info('''Loading features from cached file %s''' ,							lowercase_	)
											lowerCAmelCase__      			=						torch.load(lowercase_	)
											lowerCAmelCase__      			=						torch.tensor([f.input_ids for f in features] ,							dtype=torch.long	)
											lowerCAmelCase__      			=						torch.tensor([f.attention_mask for f in features] ,							dtype=torch.long	)
											if features[0].token_type_ids is not None:
															lowerCAmelCase__      			=						torch.tensor([f.token_type_ids for f in features] ,							dtype=torch.long	)
											else:
															lowerCAmelCase__      			=						torch.tensor([0 for f in features] ,							dtype=torch.long	)
															# HACK(we will not use this anymore soon)
											lowerCAmelCase__      			=						torch.tensor([f.label_ids for f in features] ,							dtype=torch.long	)
											return DataLoader(
											    TensorDataset(lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_	) ,							batch_size=lowercase_	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   str	):
											"""Compute validation""" ""
											lowerCAmelCase__      			=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
											if self.config.model_type != "distilbert":
															lowerCAmelCase__      			=						(
															    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
															)  # XLM and RoBERTa don"t use token_type_ids
											lowerCAmelCase__      			=						self(**lowercase_	)
											lowerCAmelCase__			,		lowerCAmelCase__      			=						outputs[:2]
											lowerCAmelCase__      			=						logits.detach().cpu().numpy()
											lowerCAmelCase__      			=						inputs['''labels'''].detach().cpu().numpy()
											return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Any	):
											lowerCAmelCase__      			=						torch.stack([x['''val_loss'''] for x in outputs]	).mean()
											lowerCAmelCase__      			=						np.concatenate([x['''pred'''] for x in outputs] ,							axis=0	)
											lowerCAmelCase__      			=						np.argmax(lowercase_ ,							axis=2	)
											lowerCAmelCase__      			=						np.concatenate([x['''target'''] for x in outputs] ,							axis=0	)

											lowerCAmelCase__      			=						dict(enumerate(self.labels	)	)
											lowerCAmelCase__      			=						[[] for _ in range(out_label_ids.shape[0]	)]
											lowerCAmelCase__      			=						[[] 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__      			=						{
											    '''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__      			=						dict(results.items()	)
											lowerCAmelCase__      			=						results
											return ret, preds_list, out_label_list




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Any	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self._eval_end(lowercase_	)
											lowerCAmelCase__      			=						ret['''log''']
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						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__      			=						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 						__snake_case					(   SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											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__":
			_UpperCAmelCase			:		Union[str, Any]   				=    argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			_UpperCAmelCase			:		Dict   				=    NERTransformer.add_model_specific_args(parser, os.getcwd())
			_UpperCAmelCase			:		int   				=    parser.parse_args()
			_UpperCAmelCase			:		Dict   				=    NERTransformer(args)
			_UpperCAmelCase			:		List[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
						_UpperCAmelCase			:		Union[str, Any]   				=    sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
						_UpperCAmelCase			:		int   				=    model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)













import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class  lowerCAmelCase_       :




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int	):
											if dst_width < 0 or dst_height < 0:
															raise ValueError('''Destination width/height should be > 0'''	)

											lowerCAmelCase__      			=						img
											lowerCAmelCase__      			=						img.shape[1]
											lowerCAmelCase__      			=						img.shape[0]
											lowerCAmelCase__      			=						dst_width
											lowerCAmelCase__      			=						dst_height

											lowerCAmelCase__      			=						self.src_w / self.dst_w
											lowerCAmelCase__      			=						self.src_h / self.dst_h

											lowerCAmelCase__      			=						lowerCAmelCase__      			=						(
											    np.ones((self.dst_h, self.dst_w, 3) ,							np.uinta	) * 255
											)




							def 						__snake_case					(   self     :   List[str]	):
											for i in range(self.dst_h	):
															for j in range(self.dst_w	):
																			lowerCAmelCase__      			=						self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_x * x	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_y * y	)


if __name__ == "__main__":
			_UpperCAmelCase				,   _UpperCAmelCase			:		List[str]   				=    800, 600
			_UpperCAmelCase			:		Tuple   				=    imread("image_data/lena.jpg", 1)
			_UpperCAmelCase			:		str   				=    NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
			)
			waitKey(0)
			destroyAllWindows()





