




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowerCamelCase       :	Dict									=     logging.getLogger(__name__)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''sequence-classification'''

 def __init__(       self      :						Optional[Any]		,							A_      :						int						)     ->			Tuple:

       """simple docstring"""






       if type(A_						) == dict:
             lowerCamelCase_	  =					Namespace(**A_						)
       lowerCamelCase_	  =					glue_output_modes[hparams.task]
       lowerCamelCase_	  =					glue_tasks_num_labels[hparams.task]

       super().__init__(A_		,							A_		,							self.mode						)

 def        a__				(       self      :						List[Any]		,							**A_      :						str						)     ->			List[Any]:

       """simple docstring"""






       return self.model(**A_						)

 def        a__				(       self      :						int		,							A_      :						Any		,							A_      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

       if self.config.model_type not in ["distilbert", "bart"]:
             lowerCamelCase_	  =					batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

       lowerCamelCase_	  =					self(**A_						)
       lowerCamelCase_	  =					outputs[0]

       lowerCamelCase_	  =					self.trainer.lr_schedulers[0]['scheduler']
       lowerCamelCase_	  =					{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss, "log": tensorboard_logs}

 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.hparams
       lowerCamelCase_	  =					processors[args.task]()
       lowerCamelCase_	  =					processor.get_labels()

       for mode in ["train", "dev"]:
             lowerCamelCase_	  =					self._feature_file(A_						)
             if os.path.exists(A_						) and not args.overwrite_cache:
                   logger.info('Loading features from cached file %s'		,							A_						)
             else:
                   logger.info('Creating features from dataset file at %s'		,							args.data_dir						)
                   lowerCamelCase_	  =					(
                       processor.get_dev_examples(args.data_dir						)
                       if mode == 'dev'
                       else processor.get_train_examples(args.data_dir						)
                   )
                   lowerCamelCase_	  =					convert_examples_to_features(
                       A_		,							self.tokenizer		,							max_length=args.max_seq_length		,							label_list=self.labels		,							output_mode=args.glue_output_mode		,							)
                   logger.info('Saving features into cached file %s'		,							A_						)
                   torch.save(A_		,							A_						)

 def        a__				(       self      :						Dict		,							A_      :						str		,							A_      :						int		,							A_      :						bool = False						)     ->			DataLoader:

       """simple docstring"""






       lowerCamelCase_	  =					'dev' if mode == 'test' else mode

       lowerCamelCase_	  =					self._feature_file(A_						)
       logger.info('Loading features from cached file %s'		,							A_						)
       lowerCamelCase_	  =					torch.load(A_						)
       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						)
       lowerCamelCase_	  =					torch.tensor([f.token_type_ids for f in features]		,							dtype=torch.long						)
       if self.hparams.glue_output_mode == "classification":
             lowerCamelCase_	  =					torch.tensor([f.label for f in features]		,							dtype=torch.long						)
       elif self.hparams.glue_output_mode == "regression":
             lowerCamelCase_	  =					torch.tensor([f.label for f in features]		,							dtype=torch.float						)

       return DataLoader(
           TensorDataset(A_		,							A_		,							A_		,							A_						)		,							batch_size=A_		,							shuffle=A_		,							)

 def        a__				(       self      :						Tuple		,							A_      :						Any		,							A_      :						Optional[int]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

       if self.config.model_type not in ["distilbert", "bart"]:
             lowerCamelCase_	  =					batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

       lowerCamelCase_	  =					self(**A_						)
       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        a__				(       self      :						List[Any]		,							A_      :						Dict						)     ->			tuple:

       """simple docstring"""






       lowerCamelCase_	  =					torch.stack([x['val_loss'] for x in outputs]						).mean().detach().cpu().item()
       lowerCamelCase_	  =					np.concatenate([x['pred'] for x in outputs]		,							axis=0						)

       if self.hparams.glue_output_mode == "classification":
             lowerCamelCase_	  =					np.argmax(A_		,							axis=1						)
       elif self.hparams.glue_output_mode == "regression":
             lowerCamelCase_	  =					np.squeeze(A_						)

       lowerCamelCase_	  =					np.concatenate([x['target'] for x in outputs]		,							axis=0						)
       lowerCamelCase_	  =					[[] for _ in range(out_label_ids.shape[0]						)]
       lowerCamelCase_	  =					[[] for _ in range(out_label_ids.shape[0]						)]

       lowerCamelCase_	  =					{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,							A_		,							A_						)}

       lowerCamelCase_	  =					dict(results.items()						)
       lowerCamelCase_	  =					results
       return ret, preds_list, out_label_list

 def        a__				(       self      :						Union[str, Any]		,							A_      :						list						)     ->			dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._eval_end(A_						)
       lowerCamelCase_	  =					ret['log']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

 def        a__				(       self      :						List[str]		,							A_      :						Union[str, Any]						)     ->			dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._eval_end(A_						)
       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        a__				(       A_      :						Optional[Any]		,							A_      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       BaseTransformer.add_model_specific_args(A_		,							A_						)
       parser.add_argument(
           '--max_seq_length'		,							default=128		,							type=A_		,							help=(
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )		,							)

       parser.add_argument(
           '--task'		,							default=''		,							type=A_		,							required=A_		,							help='The GLUE task to run'		,							)
       parser.add_argument(
           '--gpus'		,							default=0		,							type=A_		,							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

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					argparse.ArgumentParser()
      add_generic_args(lowercase			,							os.getcwd()  )
      lowerCamelCase_	  =					GLUETransformer.add_model_specific_args(lowercase			,							os.getcwd()  )
      lowerCamelCase_	  =					parser.parse_args()

      # If output_dir not provided, a folder will be generated in pwd
      if args.output_dir is None:
            lowerCamelCase_	  =					os.path.join(
                './results'			,							f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S"  )}"""			,							)
            os.makedirs(args.output_dir  )

      lowerCamelCase_	  =					GLUETransformer(lowercase  )
      lowerCamelCase_	  =					generic_train(lowercase			,							lowercase  )

      # Optionally, predict on dev set and write to output_dir
      if args.do_predict:
            lowerCamelCase_	  =					sorted(glob.glob(os.path.join(args.output_dir			,							'checkpoint-epoch=*.ckpt'  )			,							recursive=lowercase  )  )
            lowerCamelCase_	  =					model.load_from_checkpoint(checkpoints[-1]  )
            return trainer.test(lowercase  )


if __name__ == "__main__":
 main()









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)		,							0						)
                         color_img.itemset((y, x, 1)		,							0						)
                         color_img.itemset((y, x, 2)		,							255						)
       return color_img, corner_list


if __name__ == "__main__":
 lowerCamelCase       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



