



'''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


UpperCamelCase__ :			int												=				logging.getLogger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Dict    					=	'''sequence-classification'''

			def __init__(							self  :				Optional[int]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					if type(lowerCAmelCase__		) == dict:
							__SCREAMING_SNAKE_CASE :	Dict					 =	Namespace(**lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	glue_output_modes[hparams.task]
					__SCREAMING_SNAKE_CASE :	Dict					 =	glue_tasks_num_labels[hparams.task]

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

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					return self.model(**lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self(**lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	outputs[0]

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

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.hparams
					__SCREAMING_SNAKE_CASE :	Tuple					 =	processors[args.task]()
					__SCREAMING_SNAKE_CASE :	int					 =	processor.get_labels()

					for mode in ["train", "dev"]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	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		)
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
									    processor.get_dev_examples(args.data_dir		)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir		)
									)
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	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 UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				bool = False		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""dev""" if mode == """test""" else mode

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

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

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self(**lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Dict					 =	outputs[:2]
					__SCREAMING_SNAKE_CASE :	Any					 =	logits.detach().cpu().numpy()
					__SCREAMING_SNAKE_CASE :	Dict					 =	inputs["""labels"""].detach().cpu().numpy()

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

			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






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

					if self.hparams.glue_output_mode == "classification":
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	np.argmax(lowerCAmelCase__							,  axis=1		)
					elif self.hparams.glue_output_mode == "regression":
							__SCREAMING_SNAKE_CASE :	int					 =	np.squeeze(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	np.concatenate([x["""target"""] for x in outputs]							,  axis=0		)
					__SCREAMING_SNAKE_CASE :	Any					 =	[[] for _ in range(out_label_ids.shape[0]		)]
					__SCREAMING_SNAKE_CASE :	Any					 =	[[] for _ in range(out_label_ids.shape[0]		)]

					__SCREAMING_SNAKE_CASE :	Any					 =	{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task							,  lowerCAmelCase__							,  lowerCAmelCase__		)}

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

			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				list		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self._eval_end(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	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 UpperCamelCase__      (							lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					BaseTransformer.add_model_specific_args(lowerCAmelCase__							,  lowerCAmelCase__		)
					parser.add_argument(
					    """--max_seq_length"""							,  default=1_2_8							,  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        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Any					 =	argparse.ArgumentParser()
		add_generic_args(_lowerCamelCase ,   os.getcwd()      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	GLUETransformer.add_model_specific_args(_lowerCamelCase ,   os.getcwd()      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	parser.parse_args()

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

		__SCREAMING_SNAKE_CASE :	str					 =	GLUETransformer(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	generic_train(_lowerCamelCase ,   _lowerCamelCase      )

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


if __name__ == "__main__":
						main()




'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)