


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


snake_case							: Dict      =   logging.getLogger(__name__)







class  snake_case_     (__snake_case   ):
					UpperCAmelCase__      :					Any					   =    'sequence-classification'
					def __init__(   self     :List[Any]    ,__snake_case     :Any   )      ->					int:




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

												super().__init__(A_    ,A_    ,self.mode   )
					def 				lowerCamelCase__(   self     :Tuple    ,**__snake_case     :Union[str, Any]   )      ->					Union[str, Any]:
												return self.model(**A_   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :List[str]    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

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

												a__    =  self.trainer.lr_schedulers[0]["scheduler"]
												a__    =  {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss, "log": tensorboard_logs}
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												a__    =  self.hparams
												a__    =  processors[args.task]()
												a__    =  processor.get_labels()

												for mode in ["train", "dev"]:
																			a__    =  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   )
																										a__    =  (
																										    processor.get_dev_examples(args.data_dir   )
																										    if mode == "dev"
																										    else processor.get_train_examples(args.data_dir   )
																										)
																										a__    =  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 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Tuple    ,__snake_case     :Optional[Any]    ,__snake_case     :int = False   )      ->					Union[str, Any]:
												a__    =  "dev" if mode == "test" else mode

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

												return DataLoader(
												    TensorDataset(A_    ,A_    ,A_    ,A_   )    ,batch_size=A_    ,shuffle=A_    ,)
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union[str, Any]    ,__snake_case     :List[str]   )      ->					Optional[Any]:
												a__    =  {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

												a__    =  self(**A_   )
												a__    =  outputs[:2]
												a__    =  logits.detach().cpu().numpy()
												a__    =  inputs["labels"].detach().cpu().numpy()

												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :int   )      ->					Any:
												a__    =  torch.stack([x['val_loss'] for x in outputs]   ).mean().detach().cpu().item()
												a__    =  np.concatenate([x['pred'] for x in outputs]    ,axis=0   )

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

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

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

												a__    =  dict(results.items()   )
												a__    =  results
												return ret, preds_list, out_label_list
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :int   )      ->					int:
												a__    =  self._eval_end(A_   )
												a__    =  ret["log"]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :List[str]   )      ->					Optional[Any]:
												a__    =  self._eval_end(A_   )
												a__    =  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__(   __snake_case     :List[Any]    ,__snake_case     :int   )      ->					Optional[Any]:
												BaseTransformer.add_model_specific_args(A_    ,A_   )
												parser.add_argument(
												    '--max_seq_length'    ,default=1_28    ,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 							__lowercase     (				):
							a__    =  argparse.ArgumentParser()
							add_generic_args(_lowerCAmelCase      ,					os.getcwd()     )
							a__    =  GLUETransformer.add_model_specific_args(_lowerCAmelCase      ,					os.getcwd()     )
							a__    =  parser.parse_args()

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

							a__    =  GLUETransformer(_lowerCAmelCase     )
							a__    =  generic_train(_lowerCAmelCase      ,					_lowerCAmelCase     )

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


if __name__ == "__main__":
			main()








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




