
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    :      int          =			logging.getLogger(__name__)







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"sequence-classification"





	def __init__(			self				,       UpperCamelCase__		):
					if type(UpperCamelCase__		) == dict:
									A__  :	Any         =							Namespace(**UpperCamelCase__		)
					A__  :	int         =							glue_output_modes[hparams.task]
					A__  :	int         =							glue_tasks_num_labels[hparams.task]

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





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return self.model(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Dict         =							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

					A__  :	Optional[int]         =							self(**UpperCamelCase__		)
					A__  :	str         =							outputs[0]

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





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.hparams
					A__  :	Dict         =							processors[args.task]()
					A__  :	Any         =							processor.get_labels()

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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = False		):
					A__  :	Tuple         =							'''dev''' if mode == '''test''' else mode

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

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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Optional[int]         =							{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

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

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





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[Any]         =							torch.stack([x['''val_loss'''] for x in outputs]		).mean().detach().cpu().item()
					A__  :	Union[str, Any]         =							np.concatenate([x['''pred'''] for x in outputs]				,       axis=0		)

					if self.hparams.glue_output_mode == "classification":
									A__  :	Optional[Any]         =							np.argmax(UpperCamelCase__				,       axis=1		)
					elif self.hparams.glue_output_mode == "regression":
									A__  :	List[Any]         =							np.squeeze(UpperCamelCase__		)

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

					A__  :	Optional[Any]         =							{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task				,       UpperCamelCase__				,       UpperCamelCase__		)}

					A__  :	List[Any]         =							dict(results.items()		)
					A__  :	Optional[Any]         =							results
					return ret, preds_list, out_label_list





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__       ,							A__  :	Tuple         =							self._eval_end(UpperCamelCase__		)
					A__  :	Optional[int]         =							ret['''log''']
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__       ,							A__  :	Union[str, Any]         =							self._eval_end(UpperCamelCase__		)
					A__  :	Dict         =							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   __snake_case				(			UpperCamelCase__				,       UpperCamelCase__		):
					BaseTransformer.add_model_specific_args(UpperCamelCase__				,       UpperCamelCase__		)
					parser.add_argument(
					    '''--max_seq_length'''				,       default=128				,       type=UpperCamelCase__				,       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=UpperCamelCase__				,       required=UpperCamelCase__				,       help='''The GLUE task to run'''				,       )
					parser.add_argument(
					    '''--gpus'''				,       default=0				,       type=UpperCamelCase__				,       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 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

				"""simple docstring"""







				A__  :	List[Any]         =							argparse.ArgumentParser()
				add_generic_args(__UpperCamelCase							,						os.getcwd()       )
				A__  :	Any         =							GLUETransformer.add_model_specific_args(__UpperCamelCase							,						os.getcwd()       )
				A__  :	Tuple         =							parser.parse_args()

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

				A__  :	List[Any]         =							GLUETransformer(__UpperCamelCase       )
				A__  :	List[str]         =							generic_train(__UpperCamelCase							,						__UpperCamelCase       )

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


if __name__ == "__main__":
					main()

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				for i in range(1							,						10_01       ):
								total += i**i
				return str(__UpperCamelCase       )[-10:]


if __name__ == "__main__":
					print(solution())