


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     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  'sequence-classification'






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




         """simple docstring"""







         if type(_lowercase ) == dict:
                UpperCAmelCase__										=     Namespace(**_lowercase )
         UpperCAmelCase__										=     glue_output_modes[hparams.task]
         UpperCAmelCase__										=     glue_tasks_num_labels[hparams.task]

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






  def 			_UpperCAmelCase  (					self      :					str		,						**_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         return self.model(**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					List[str] ):




         """simple docstring"""







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

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

         UpperCAmelCase__										=     self(**_lowercase )
         UpperCAmelCase__										=     outputs[0]

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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.hparams
         UpperCAmelCase__										=     processors[args.task]()
         UpperCAmelCase__										=     processor.get_labels()

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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					int		,						_lowercase      :					bool = False ):




         """simple docstring"""







         UpperCAmelCase__										=     "dev" if mode == "test" else mode

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

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






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					int ):




         """simple docstring"""







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

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

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

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






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					Optional[Any] ):




         """simple docstring"""







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

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

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

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

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






  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					list ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     self._eval_end(_lowercase )
         UpperCAmelCase__										=     ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     self._eval_end(_lowercase )
         UpperCAmelCase__										=     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  (					_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple ):




         """simple docstring"""







         BaseTransformer.add_model_specific_args(_lowercase		,						_lowercase )
         parser.add_argument(
             "--max_seq_length"		,						default=1_28		,						type=_lowercase		,						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=_lowercase		,						required=_lowercase		,						help="The GLUE task to run"		,						)
         parser.add_argument(
             "--gpus"		,						default=0		,						type=_lowercase		,						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       (		)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser()
       add_generic_args(__A ,						os.getcwd()			)
       UpperCAmelCase__										=     GLUETransformer.add_model_specific_args(__A ,						os.getcwd()			)
       UpperCAmelCase__										=     parser.parse_args()

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

       UpperCAmelCase__										=     GLUETransformer(__A			)
       UpperCAmelCase__										=     generic_train(__A ,						__A			)

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


if __name__ == "__main__":
    main()









import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







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





