
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								=  logging.getLogger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Optional[int]				       ="""sequence-classification"""
	def __init__(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[str, Any]							):
						if type(UpperCAmelCase_							) == dict:
											SCREAMING_SNAKE_CASE__													= Namespace(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= glue_output_modes[hparams.task]
						SCREAMING_SNAKE_CASE__													= glue_tasks_num_labels[hparams.task]

						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.mode							)
	def      A_				(			self							:   Optional[int]       ,   **UpperCAmelCase_							:   Optional[int]							):
						return self.model(**UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

						SCREAMING_SNAKE_CASE__													= self(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= outputs[0]

						SCREAMING_SNAKE_CASE__													= self.trainer.lr_schedulers[0]['scheduler']
						SCREAMING_SNAKE_CASE__													= {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss, "log": tensorboard_logs}
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.hparams
						SCREAMING_SNAKE_CASE__													= processors[args.task]()
						SCREAMING_SNAKE_CASE__													= processor.get_labels()

						for mode in ["train", "dev"]:
											SCREAMING_SNAKE_CASE__													= 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							)
																SCREAMING_SNAKE_CASE__													= (
																    processor.get_dev_examples(args.data_dir							)
																    if mode == 'dev'
																    else processor.get_train_examples(args.data_dir							)
																)
																SCREAMING_SNAKE_CASE__													= 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      A_				(			self							:   int       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   bool = False							):
						SCREAMING_SNAKE_CASE__													= 'dev' if mode == 'test' else mode

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

						return DataLoader(
						    TensorDataset(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)       ,   batch_size=UpperCAmelCase_       ,   shuffle=UpperCAmelCase_       ,   )
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__													= {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

						SCREAMING_SNAKE_CASE__													= self(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= outputs[:2]
						SCREAMING_SNAKE_CASE__													= logits.detach().cpu().numpy()
						SCREAMING_SNAKE_CASE__													= inputs['labels'].detach().cpu().numpy()

						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= torch.stack([x['val_loss'] for x in outputs]							).mean().detach().cpu().item()
						SCREAMING_SNAKE_CASE__													= np.concatenate([x['pred'] for x in outputs]       ,   axis=0							)

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

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

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

						SCREAMING_SNAKE_CASE__													= dict(results.items()							)
						SCREAMING_SNAKE_CASE__													= results
						return ret, preds_list, out_label_list
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   list							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._eval_end(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= ret['log']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   Tuple							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._eval_end(UpperCAmelCase_							)
						SCREAMING_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      A_				(			UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict							):
						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 							_lowercase							(     )		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= argparse.ArgumentParser()
					add_generic_args(UpperCamelCase_	,	os.getcwd()		)
					SCREAMING_SNAKE_CASE__													= GLUETransformer.add_model_specific_args(UpperCamelCase_	,	os.getcwd()		)
					SCREAMING_SNAKE_CASE__													= parser.parse_args()

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

					SCREAMING_SNAKE_CASE__													= GLUETransformer(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= generic_train(UpperCamelCase_	,	UpperCamelCase_		)

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


if __name__ == "__main__":
				main()







import random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





