



'''simple docstring'''
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_					:      str    			=    logging.getLogger(__name__)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     """sequence-classification"""





	def __init__(					self   :							str					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							if type(lowerCamelCase__					) == dict:
													_UpperCamelCase   :						Optional[Any]												=  Namespace(**lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  glue_output_modes[hparams.task]
							_UpperCamelCase   :						List[str]												=  glue_tasks_num_labels[hparams.task]

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





	def       UpperCamelCase_    (					self   :							Tuple					,**lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							return self.model(**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

							_UpperCamelCase   :						str												=  self(**lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  outputs[0]

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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  self.hparams
							_UpperCamelCase   :						List[str]												=  processors[args.task]()
							_UpperCamelCase   :						Any												=  processor.get_labels()

							for mode in ["train", "dev"]:
													_UpperCamelCase   :						Optional[int]												=  self._feature_file(lowerCamelCase__					)
													if os.path.exists(lowerCamelCase__					) and not args.overwrite_cache:
																			logger.info('Loading features from cached file %s'					,lowerCamelCase__					)
													else:
																			logger.info('Creating features from dataset file at %s'					,args.data_dir					)
																			_UpperCamelCase   :						Optional[int]												=  (
																			    processor.get_dev_examples(args.data_dir					)
																			    if mode == 'dev'
																			    else processor.get_train_examples(args.data_dir					)
																			)
																			_UpperCamelCase   :						str												=  convert_examples_to_features(
																			    lowerCamelCase__					,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'					,lowerCamelCase__					)
																			torch.save(lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							int					,lowerCamelCase__   :							bool = False					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  'dev' if mode == 'test' else mode

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

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





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

							_UpperCamelCase   :						Optional[int]												=  self(**lowerCamelCase__					)
							_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  outputs[:2]
							_UpperCamelCase   :						Union[str, Any]												=  logits.detach().cpu().numpy()
							_UpperCamelCase   :						Any												=  inputs['labels'].detach().cpu().numpy()

							return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  torch.stack([x['val_loss'] for x in outputs]					).mean().detach().cpu().item()
							_UpperCamelCase   :						Tuple												=  np.concatenate([x['pred'] for x in outputs]					,axis=0					)

							if self.hparams.glue_output_mode == "classification":
													_UpperCamelCase   :						Optional[int]												=  np.argmax(lowerCamelCase__					,axis=1					)
							elif self.hparams.glue_output_mode == "regression":
													_UpperCamelCase   :						str												=  np.squeeze(lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  np.concatenate([x['target'] for x in outputs]					,axis=0					)
							_UpperCamelCase   :						List[str]												=  [[] for _ in range(out_label_ids.shape[0]					)]
							_UpperCamelCase   :						Dict												=  [[] for _ in range(out_label_ids.shape[0]					)]

							_UpperCamelCase   :						Tuple												=  {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task					,lowerCamelCase__					,lowerCamelCase__					)}

							_UpperCamelCase   :						int												=  dict(results.items()					)
							_UpperCamelCase   :						int												=  results
							return ret, preds_list, out_label_list





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							list					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  self._eval_end(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  ret['log']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							Any					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  self._eval_end(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, 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       UpperCamelCase_    (					lowerCamelCase__   :							Any					,lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							BaseTransformer.add_model_specific_args(lowerCamelCase__					,lowerCamelCase__					)
							parser.add_argument(
							    '--max_seq_length'					,default=128					,type=lowerCamelCase__					,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=lowerCamelCase__					,required=lowerCamelCase__					,help='The GLUE task to run'					,)
							parser.add_argument(
							    '--gpus'					,default=0					,type=lowerCamelCase__					,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 A__       (	):
						_UpperCamelCase   :						Any												=  argparse.ArgumentParser()
						add_generic_args(UpperCAmelCase_    ,    os.getcwd()				)
						_UpperCamelCase   :						List[Any]												=  GLUETransformer.add_model_specific_args(UpperCAmelCase_    ,    os.getcwd()				)
						_UpperCamelCase   :						Any												=  parser.parse_args()

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

						_UpperCamelCase   :						List[Any]												=  GLUETransformer(UpperCAmelCase_				)
						_UpperCamelCase   :						Optional[int]												=  generic_train(UpperCAmelCase_    ,    UpperCAmelCase_				)

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


if __name__ == "__main__":
					main()







'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


