
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


lowerCamelCase__          =		logging.getLogger(__name__)







class      A__							(     __magic_name__	):
				lowercase			    =  'token-classification'






				def __init__(						self					:      str    ,     a					:      List[Any]   ):






							'''simple docstring'''







							if type(a   ) == dict:
										lowerCAmelCase__      :      Dict         =		Namespace(**a   )
							lowerCAmelCase__      :      Optional[int]         =		import_module('tasks'   )
							try:
										lowerCAmelCase__      :      Dict         =		getattr(a    ,     hparams.task_type   )
										lowerCAmelCase__      :      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__()}'''   )
							lowerCAmelCase__      :      Any         =		self.token_classification_task.get_labels(hparams.labels   )
							lowerCAmelCase__      :      Optional[Any]         =		CrossEntropyLoss().ignore_index
							super().__init__(a    ,     len(self.labels   )    ,     self.mode   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     **a					:      Any   ):






							'''simple docstring'''







							return self.model(**a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Union[str, Any]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
							if self.config.model_type != "distilbert":
										lowerCAmelCase__      :      Any         =		(
										    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
										)  # XLM and RoBERTa don"t use token_type_ids

							lowerCAmelCase__      :      List[str]         =		self(**a   )
							lowerCAmelCase__      :      Optional[Any]         =		outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      int    ,     a					:      int    ,     a					:      bool = False   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Any    ,     a					:      Optional[int]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		torch.stack([x['val_loss'] for x in outputs]   ).mean()
							lowerCAmelCase__      :      str         =		np.concatenate([x['pred'] for x in outputs]    ,     axis=0   )
							lowerCAmelCase__      :      List[str]         =		np.argmax(a    ,     axis=2   )
							lowerCAmelCase__      :      Dict         =		np.concatenate([x['target'] for x in outputs]    ,     axis=0   )

							lowerCAmelCase__      :      List[str]         =		dict(enumerate(self.labels   )   )
							lowerCAmelCase__      :      Optional[int]         =		[[] for _ in range(out_label_ids.shape[0]   )]
							lowerCAmelCase__      :      List[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]]   )

							lowerCAmelCase__      :      str         =		{
							    'val_loss': val_loss_mean,
							    'accuracy_score': accuracy_score(a    ,     a   ),
							    'precision': precision_score(a    ,     a   ),
							    'recall': recall_score(a    ,     a   ),
							    'f1': fa_score(a    ,     a   ),
							}

							lowerCAmelCase__      :      Union[str, Any]         =		dict(results.items()   )
							lowerCAmelCase__      :      Union[str, Any]         =		results
							return ret, preds_list, out_label_list






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		self._eval_end(a   )
							lowerCAmelCase__      :      Optional[Any]         =		ret['log']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[Any]         =		self._eval_end(a   )

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							lowerCAmelCase__      :      int         =		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		(						a					:      Dict    ,     a					:      int   ):






							'''simple docstring'''







							BaseTransformer.add_model_specific_args(a    ,     a   )
							parser.add_argument(
							    '--task_type'    ,     default='NER'    ,     type=a    ,     help='Task type to fine tune in training (e.g. NER, POS, etc)'   )
							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(
							    '--labels'    ,     default=''    ,     type=a    ,     help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'    ,     )
							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


if __name__ == "__main__":
					lowerCamelCase__          =		argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					lowerCamelCase__          =		NERTransformer.add_model_specific_args(parser, os.getcwd())
					lowerCamelCase__          =		parser.parse_args()
					lowerCamelCase__          =		NERTransformer(args)
					lowerCamelCase__          =		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
										lowerCamelCase__          =		sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
										lowerCamelCase__          =		model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)

def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		list:

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

			lowerCAmelCase__      :      Optional[Any]         =		[]

			def generate(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						if k == 1:
									res.append(tuple(arr[:]     )     )
									return

						generate(k - 1       ,							SCREAMING_SNAKE_CASE_     )

						for i in range(k - 1     ):
									if k % 2 == 0:  # k is even
												lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		arr[k - 1], arr[i]
									else:  # k is odd
												lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		arr[k - 1], arr[0]
									generate(k - 1       ,							SCREAMING_SNAKE_CASE_     )

			generate(len(SCREAMING_SNAKE_CASE_     )       ,							SCREAMING_SNAKE_CASE_     )
			return res


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