




'''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 : Any  				=						logging.getLogger(__name__)

class      A       (						_a       ):
							lowercase_							     =							'sequence-classification'







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    List[str]   )     ->				Optional[int]:


														"""simple docstring"""


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

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







							def   __lowerCAmelCase     (    self			:    Any				,     **lowerCAmelCase_			:    Any   )     ->				int:


														"""simple docstring"""


														return self.model(**lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				Union[str, Any]:


														"""simple docstring"""


														_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(**lowerCAmelCase_   )
														_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			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


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

														for mode in ["train", "dev"]:
																					_a      	=							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   )
																												_a      	=							(
																												    processor.get_dev_examples(args.data_dir   )
																												    if mode == '''dev'''
																												    else processor.get_train_examples(args.data_dir   )
																												)
																												_a      	=							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   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    bool = False   )     ->				DataLoader:


														"""simple docstring"""


														_a      	=							'''dev''' if mode == '''test''' else mode

														_a      	=							self._feature_file(lowerCAmelCase_   )
														logger.info('''Loading features from cached file %s'''				,     lowerCAmelCase_   )
														_a      	=							torch.load(lowerCAmelCase_   )
														_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(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     batch_size=lowerCAmelCase_				,     shuffle=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Any   )     ->				Optional[int]:


														"""simple docstring"""


														_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(**lowerCAmelCase_   )
														_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			:    Tuple				,     lowerCAmelCase_			:    Any   )     ->				tuple:


														"""simple docstring"""


														_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(lowerCAmelCase_				,     axis=1   )
														elif self.hparams.glue_output_mode == "regression":
																					_a      	=							np.squeeze(lowerCAmelCase_   )

														_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				,     lowerCAmelCase_				,     lowerCAmelCase_   )}

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







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    list   )     ->				dict:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    str   )     ->				dict:


														"""simple docstring"""


														_a	,				_a	,				_a      	=							self._eval_end(lowerCAmelCase_   )
														_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     (    lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														BaseTransformer.add_model_specific_args(lowerCAmelCase_				,     lowerCAmelCase_   )
														parser.add_argument(
														    '''--max_seq_length'''				,     default=1_28				,     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 						snake_case_       ():





							'''simple docstring'''


							_a      	=							argparse.ArgumentParser()
							add_generic_args(UpperCamelCase					,		os.getcwd() )
							_a      	=							GLUETransformer.add_model_specific_args(UpperCamelCase					,		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(UpperCamelCase )
							_a      	=							generic_train(UpperCamelCase					,		UpperCamelCase )

							# 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=UpperCamelCase ) )
														_a      	=							model.load_from_checkpoint(checkpoints[-1] )
														return trainer.test(UpperCamelCase )


if __name__ == "__main__":
				main()





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       if len(_A       ) <= 1:
              return [tuple(_A       )]

       _lowerCAmelCase     :				List[str]       =							[]

       def generate(_A ,    _A       ):
              _lowerCAmelCase     :				Any       =							[0] * n
              res.append(tuple(_A       )       )

              _lowerCAmelCase     :				Any       =							0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							arr[i], arr[0]
                            else:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							arr[i], arr[c[i]]
                            res.append(tuple(_A       )       )
                            c[i] += 1
                            _lowerCAmelCase     :				Optional[int]       =							0
                     else:
                            _lowerCAmelCase     :				Any       =							0
                            i += 1

       generate(len(_A       ) ,    _A       )
       return res


if __name__ == "__main__":
       lowerCAmelCase     :  int											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Optional[int]											=    [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))

