





"""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	     =					logging.getLogger(__name__)




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''token-classification'''







					def __init__(  self		,     _UpperCAmelCase       ):
									if type(_UpperCAmelCase       ) == dict:
													__a	:  Any         =					Namespace(**_UpperCAmelCase       )
									__a	:  Optional[int]         =					import_module('''tasks'''       )
									try:
													__a	:  int         =					getattr(_UpperCAmelCase		,     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	:  Dict         =					self.token_classification_task.get_labels(hparams.labels       )
									__a	:  Union[str, Any]         =					CrossEntropyLoss().ignore_index
									super().__init__(_UpperCAmelCase		,     len(self.labels       )		,     self.mode       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									return self.model(**_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  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	:  str         =					self(**_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					outputs[0]
									# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
									return {"loss": loss}







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					self.hparams
									for mode in ["train", "dev", "test"]:
													__a	:  Tuple         =					self._feature_file(_UpperCAmelCase       )
													if os.path.exists(_UpperCAmelCase       ) and not args.overwrite_cache:
																	logger.info('''Loading features from cached file %s'''		,     _UpperCAmelCase       )
																	__a	:  str         =					torch.load(_UpperCAmelCase       )
													else:
																	logger.info('''Creating features from dataset file at %s'''		,     args.data_dir       )
																	__a	:  str         =					self.token_classification_task.read_examples_from_file(args.data_dir		,     _UpperCAmelCase       )
																	__a	:  Optional[Any]         =					self.token_classification_task.convert_examples_to_features(
																	    _UpperCAmelCase		,     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=_UpperCAmelCase		,     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'''		,     _UpperCAmelCase       )
																	torch.save(_UpperCAmelCase		,     _UpperCAmelCase       )







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







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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					torch.stack([x['''val_loss'''] for x in outputs]       ).mean()
									__a	:  List[str]         =					np.concatenate([x['''pred'''] for x in outputs]		,     axis=0       )
									__a	:  str         =					np.argmax(_UpperCAmelCase		,     axis=2       )
									__a	:  List[str]         =					np.concatenate([x['''target'''] for x in outputs]		,     axis=0       )

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

									__a	:  str         =					dict(results.items()       )
									__a	:  Tuple         =					results
									return ret, preds_list, out_label_list







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									# when stable
									__a    ,  __a    ,  __a	:  Any         =					self._eval_end(_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									# updating to test_epoch_end instead of deprecated test_end
									__a    ,  __a    ,  __a	:  int         =					self._eval_end(_UpperCAmelCase       )

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






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				return (gray > 1_27) & (gray <= 2_55)

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

				# Copy image to padded image
				__a	:  int         =					image

				# Iterate over image & apply kernel
				for x in range(image.shape[1]):
								for y in range(image.shape[0]):
												__a	:  Optional[Any]         =					(
												    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
												).sum()
												__a	:  Any         =					int(summation > 0)
				return output


if __name__ == "__main__":
		# read original image
		A	     =					Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
		A	     =					np.array(Image.open(lena_path))
		# kernel to be applied
		A	     =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
		A	     =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
		# Save the output image
		A	     =					Image.fromarray(output).convert('''RGB''')
		pil_img.save('''result_dilation.png''')