


'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCamelCase							:   Any  						=	{
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Dict  						=	[
     'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GroupViTModel',
     'GroupViTPreTrainedModel',
     'GroupViTTextModel',
     'GroupViTVisionModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Tuple  						=	[
     'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFGroupViTModel',
     'TFGroupViTPreTrainedModel',
     'TFGroupViTTextModel',
     'TFGroupViTVisionModel',
 ]

if TYPE_CHECKING:
 from .configuration_groupvit import (
     GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GroupViTConfig,
     GroupViTOnnxConfig,
     GroupViTTextConfig,
     GroupViTVisionConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_groupvit import (
      GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      GroupViTModel,
      GroupViTPreTrainedModel,
      GroupViTTextModel,
      GroupViTVisionModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_groupvit import (
      TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGroupViTModel,
      TFGroupViTPreTrainedModel,
      TFGroupViTTextModel,
      TFGroupViTVisionModel,
  )

else:
 import sys

 lowerCamelCase							:   Dict  						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










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


SCREAMING_SNAKE_CASE__       :      Dict  =   logging.getLogger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """sequence-classification"""







    def __init__(    self     ,  UpperCamelCase__     )     ->      List[Any]:
           if type(UpperCamelCase__     ) == dict:
                  lowerCamelCase   :      int			 =					Namespace(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					glue_output_modes[hparams.task]
           lowerCamelCase   :      int			 =					glue_tasks_num_labels[hparams.task]

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







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Tuple:
           return self.model(**UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           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   :      List[str]			 =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

           lowerCamelCase   :      Optional[int]			 =					self(**UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					outputs[0]

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







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Any			 =					self.hparams
           lowerCamelCase   :      Union[str, Any]			 =					processors[args.task]()
           lowerCamelCase   :      Optional[int]			 =					processor.get_labels()

           for mode in ["train", "dev"]:
                  lowerCamelCase   :      Optional[Any]			 =					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     )
                         lowerCamelCase   :      List[str]			 =					(
                             processor.get_dev_examples(args.data_dir     )
                             if mode == "dev"
                             else processor.get_train_examples(args.data_dir     )
                         )
                         lowerCamelCase   :      Dict			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     )     ->      DataLoader:
           lowerCamelCase   :      str			 =					"dev" if mode == "test" else mode

           lowerCamelCase   :      int			 =					self._feature_file(UpperCamelCase__     )
           logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					torch.load(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.input_ids for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      str			 =					torch.tensor([f.attention_mask for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.token_type_ids for f in features]     ,  dtype=torch.long     )
           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Any			 =					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(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ,  batch_size=UpperCamelCase__     ,  shuffle=UpperCamelCase__     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

           lowerCamelCase   :      Dict			 =					self(**UpperCamelCase__     )
           lowerCamelCase ,      lowerCamelCase   :      Any			 =					outputs[:2]
           lowerCamelCase   :      Union[str, Any]			 =					logits.detach().cpu().numpy()
           lowerCamelCase   :      Optional[Any]			 =					inputs["labels"].detach().cpu().numpy()

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







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      tuple:
           lowerCamelCase   :      Union[str, Any]			 =					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   :      Union[str, Any]			 =					np.argmax(UpperCamelCase__     ,  axis=1     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      str			 =					np.squeeze(UpperCamelCase__     )

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

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

           lowerCamelCase   :      List[str]			 =					dict(results.items()     )
           lowerCamelCase   :      Optional[int]			 =					results
           return ret, preds_list, out_label_list







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					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     _lowercase     (    UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           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      A      (      )	-> int:
       lowerCamelCase   :      int			 =					argparse.ArgumentParser()
       add_generic_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					GLUETransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					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   :      int			 =					GLUETransformer(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					generic_train(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
       main()






