

'''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					 =	logging.getLogger(__name__)


class 	lowerCAmelCase  (   snake_case_ ):
       lowerCAmelCase_												=			'''sequence-classification'''







       def __init__(				self      :			Tuple    ,   __lowercase      :			Optional[Any]   ):







            """simple docstring"""







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

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







       def       snake_case (				self      :			int    ,   **__lowercase      :			Tuple   ):







            """simple docstring"""







            return self.model(**UpperCamelCase__   )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			Dict   ):







            """simple docstring"""







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

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

            __lowercase      		=self(**UpperCamelCase__   )
            __lowercase      		=outputs[0]

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







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







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

            for mode in ["train", "dev"]:
                 __lowercase      		=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   )
                      __lowercase      		=(
                          processor.get_dev_examples(args.data_dir   )
                          if mode == "dev"
                          else processor.get_train_examples(args.data_dir   )
                      )
                      __lowercase      		=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       snake_case (				self      :			str    ,   __lowercase      :			Tuple    ,   __lowercase      :			Any    ,   __lowercase      :			str = False   ):







            """simple docstring"""







            __lowercase      		="dev" if mode == "test" else mode

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

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







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			str   ):







            """simple docstring"""







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

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

            __lowercase      		=self(**UpperCamelCase__   )
            __lowercase      		=outputs[:2]
            __lowercase      		=logits.detach().cpu().numpy()
            __lowercase      		=inputs["labels"].detach().cpu().numpy()

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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







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

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

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

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

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







       def       snake_case (				self      :			int    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=self._eval_end(UpperCamelCase__   )
            __lowercase      		=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       snake_case (				__lowercase      :			Optional[Any]    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            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 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=argparse.ArgumentParser()
     add_generic_args(lowercase__,       os.getcwd()			)
     __lowercase      		=GLUETransformer.add_model_specific_args(lowercase__,       os.getcwd()			)
     __lowercase      		=parser.parse_args()

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

     __lowercase      		=GLUETransformer(lowercase__			)
     __lowercase      		=generic_train(lowercase__,       lowercase__			)

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


if __name__ == "__main__":
       main()






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case       =       {
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       ["""PoolFormerFeatureExtractor"""]
  __snake_case       =       ["""PoolFormerImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PoolFormerForImageClassification""",
      """PoolFormerModel""",
      """PoolFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_poolformer import (
      POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PoolFormerConfig,
      PoolFormerOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_poolformer import PoolFormerFeatureExtractor
    from .image_processing_poolformer import PoolFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_poolformer import (
        POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PoolFormerForImageClassification,
        PoolFormerModel,
        PoolFormerPreTrainedModel,
    )


else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





