


"""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


__SCREAMING_SNAKE_CASE							:      Optional[int]						=      logging.getLogger(__name__)





class        __A       (lowercase__):





							'''simple docstring'''

							__lowercase:						Optional[Any]							 =      '''sequence-classification'''



							def __init__(			self							: Dict						,    UpperCAmelCase_							: Optional[Any]    )   ->Any:



														"""simple docstring"""



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

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



							def 						lowerCAmelCase    (			self							: Optional[int]						,    **UpperCAmelCase_							: Dict    )   ->Any:



														"""simple docstring"""



														return self.model(**_a    )



							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: Dict						,    UpperCAmelCase_							: Union[str, Any]    )   ->List[str]:



														"""simple docstring"""



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

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

														snake_case_          =						self(**_a    )
														snake_case_          =						outputs[0]

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



							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



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

														for mode in ["train", "dev"]:
																					snake_case_          =						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    )
																												snake_case_          =						(
																												    processor.get_dev_examples(args.data_dir    )
																												    if mode == 'dev'
																												    else processor.get_train_examples(args.data_dir    )
																												)
																												snake_case_          =						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						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: bool = False    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						'dev' if mode == 'test' else mode

														snake_case_          =						self._feature_file(_a    )
														logger.info("""Loading features from cached file %s"""						,    _a    )
														snake_case_          =						torch.load(_a    )
														snake_case_          =						torch.tensor([f.input_ids for f in features]						,    dtype=torch.long    )
														snake_case_          =						torch.tensor([f.attention_mask for f in features]						,    dtype=torch.long    )
														snake_case_          =						torch.tensor([f.token_type_ids for f in features]						,    dtype=torch.long    )
														if self.hparams.glue_output_mode == "classification":
																					snake_case_          =						torch.tensor([f.label for f in features]						,    dtype=torch.long    )
														elif self.hparams.glue_output_mode == "regression":
																					snake_case_          =						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							: Any						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: Union[str, Any]    )   ->Optional[int]:



														"""simple docstring"""



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

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

														snake_case_          =						self(**_a    )
														snake_case_          =						outputs[:2]
														snake_case_          =						logits.detach().cpu().numpy()
														snake_case_          =						inputs['labels'].detach().cpu().numpy()

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



							def 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: Dict    )   ->List[str]:



														"""simple docstring"""



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

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

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

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

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



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: list    )   ->List[str]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self._eval_end(_a    )
														snake_case_          =						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    (			UpperCAmelCase_							: Optional[Any]						,    UpperCAmelCase_							: int    )   ->Dict:



														"""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   _a			( )      ->			Dict:
							snake_case_          =						argparse.ArgumentParser()
							add_generic_args(__a			,	os.getcwd()			)
							snake_case_          =						GLUETransformer.add_model_specific_args(__a			,	os.getcwd()			)
							snake_case_          =						parser.parse_args()

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

							snake_case_          =						GLUETransformer(__a			)
							snake_case_          =						generic_train(__a			,	__a			)

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


if __name__ == "__main__":
				main()







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							assert column_title.isupper()
							snake_case_          =						0
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			) - 1
							snake_case_          =						0

							while index >= 0:
														snake_case_          =						(ord(column_title[index]			) - 64) * pow(26			,	_SCREAMING_SNAKE_CASE			)
														answer += value
														power += 1
														index -= 1

							return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()



