
'''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


A         =   logging.getLogger(__name__)




class      __snake_case		( a__):
					_lowerCAmelCase        =						'''sequence-classification'''
					def __init__(  self,     A   ):

									"""simple docstring"""


									if type(lowerCamelCase_   ) == dict:
													lowerCamelCase       :      Dict          =			Namespace(**lowerCamelCase_   )
									lowerCamelCase       :      List[Any]          =			glue_output_modes[hparams.task]
									lowerCamelCase       :      Optional[int]          =			glue_tasks_num_labels[hparams.task]

									super().__init__(lowerCamelCase_,     lowerCamelCase_,     self.mode   )
					def      UpperCAmelCase_				(  self,     **A   ):

									"""simple docstring"""


									return self.model(**lowerCamelCase_   )
					def      UpperCAmelCase_				(  self,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

									lowerCamelCase       :      int          =			self(**lowerCamelCase_   )
									lowerCamelCase       :      Optional[Any]          =			outputs[0]

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

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			self.hparams
									lowerCamelCase       :      str          =			processors[args.task]()
									lowerCamelCase       :      Any          =			processor.get_labels()

									for mode in ["train", "dev"]:
													lowerCamelCase       :      Tuple          =			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   )
																	lowerCamelCase       :      str          =			(
																	    processor.get_dev_examples(args.data_dir   )
																	    if mode == '''dev'''
																	    else processor.get_train_examples(args.data_dir   )
																	)
																	lowerCamelCase       :      Union[str, 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,     A,     A,     A = False   ):

									"""simple docstring"""


									lowerCamelCase       :      List[Any]          =			'''dev''' if mode == '''test''' else mode

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

									"""simple docstring"""


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

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

									lowerCamelCase       :      Tuple          =			self(**lowerCamelCase_   )
									lowerCamelCase       :      Any          =			outputs[:2]
									lowerCamelCase       :      Optional[int]          =			logits.detach().cpu().numpy()
									lowerCamelCase       :      int          =			inputs['''labels'''].detach().cpu().numpy()

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

									"""simple docstring"""


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

									if self.hparams.glue_output_mode == "classification":
													lowerCamelCase       :      Optional[int]          =			np.argmax(lowerCamelCase_,     axis=1   )
									elif self.hparams.glue_output_mode == "regression":
													lowerCamelCase       :      Optional[int]          =			np.squeeze(lowerCamelCase_   )

									lowerCamelCase       :      Optional[Any]          =			np.concatenate([x['target'] for x in outputs],     axis=0   )
									lowerCamelCase       :      Tuple          =			[[] for _ in range(out_label_ids.shape[0]   )]
									lowerCamelCase       :      Union[str, Any]          =			[[] for _ in range(out_label_ids.shape[0]   )]

									lowerCamelCase       :      Dict          =			{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,     lowerCamelCase_,     lowerCamelCase_   )}

									lowerCamelCase       :      str          =			dict(results.items()   )
									lowerCamelCase       :      Optional[Any]          =			results
									return ret, preds_list, out_label_list
					def      UpperCAmelCase_				(  self,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      str          =			self._eval_end(lowerCamelCase_   )
									lowerCamelCase       :      Optional[int]          =			ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
					def      UpperCAmelCase_				(  self,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			self._eval_end(lowerCamelCase_   )
									lowerCamelCase       :      int          =			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   ):

									"""simple docstring"""


									BaseTransformer.add_model_specific_args(lowerCamelCase_,     lowerCamelCase_   )
									parser.add_argument(
									    '--max_seq_length',     default=128,     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  UpperCAmelCase				(	):
				lowerCamelCase       :      Dict          =			argparse.ArgumentParser()
				add_generic_args(_A							,				os.getcwd())
				lowerCamelCase       :      int          =			GLUETransformer.add_model_specific_args(_A							,				os.getcwd())
				lowerCamelCase       :      List[Any]          =			parser.parse_args()

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

				lowerCamelCase       :      Union[str, Any]          =			GLUETransformer(_A)
				lowerCamelCase       :      Dict          =			generic_train(_A							,				_A)

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


if __name__ == "__main__":
					main()

'''simple docstring'''

import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




