


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


_lowercase							:   Optional[Any]						=						logging.getLogger(__name__)






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'sequence-classification'

				def __init__(    self					, a__ )						->					Union[str, Any]:
					if type(a__ ) == dict:
						A             =					Namespace(**a__ )
					A             =					glue_output_modes[hparams.task]
					A             =					glue_tasks_num_labels[hparams.task]

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

				def   _UpperCAmelCase       (    self					, **a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					int:
					A             =					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					A             =					self(**a__ )
					A             =					outputs[0]

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

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					self.hparams
					A             =					processors[args.task]()
					A             =					processor.get_labels()

					for mode in ["train", "dev"]:
						A             =					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 )
							A             =					(
							    processor.get_dev_examples(args.data_dir )
							    if mode == """dev"""
							    else processor.get_train_examples(args.data_dir )
							)
							A             =					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   _UpperCAmelCase       (    self					, a__					, a__					, a__ = False )						->					DataLoader:
					A             =					"""dev""" if mode == """test""" else mode

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

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

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					List[str]:
					A             =					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					A             =					self(**a__ )
					A       ,      A             =					outputs[:2]
					A             =					logits.detach().cpu().numpy()
					A             =					inputs["""labels"""].detach().cpu().numpy()

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

				def   _UpperCAmelCase       (    self					, a__ )						->					tuple:
					A             =					torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
					A             =					np.concatenate([x["""pred"""] for x in outputs]					, axis=0 )

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

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

					A             =					{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task					, a__					, a__ )}

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

				def   _UpperCAmelCase       (    self					, a__ )						->					dict:
					A       ,      A       ,      A             =					self._eval_end(a__ )
					A             =					ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

				def   _UpperCAmelCase       (    self					, a__ )						->					dict:
					A       ,      A       ,      A             =					self._eval_end(a__ )
					A             =					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       (    a__					, a__ )						->					List[Any]:
					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 	_lowerCAmelCase					( )			-> List[Any]:





	"""simple docstring"""







	A             =					argparse.ArgumentParser()
	add_generic_args(UpperCamelCase__				,       os.getcwd()    )
	A             =					GLUETransformer.add_model_specific_args(UpperCamelCase__				,       os.getcwd()    )
	A             =					parser.parse_args()

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

	A             =					GLUETransformer(UpperCamelCase__    )
	A             =					generic_train(UpperCamelCase__				,       UpperCamelCase__    )

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


if __name__ == "__main__":
				main()







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



