


'''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_      :		Optional[int]          =		logging.getLogger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




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




 def __init__(    self		,      lowerCamelCase_      )      ->    str:
    if type(lowerCamelCase_      ) == dict:
       _a					:  str						=  Namespace(**lowerCamelCase_      )
    _a					:  Optional[Any]						=  glue_output_modes[hparams.task]
    _a					:  Tuple						=  glue_tasks_num_labels[hparams.task]

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




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    Dict:
    return self.model(**lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    str:
    _a					:  Optional[Any]						=  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

    _a					:  Any						=  self(**lowerCamelCase_      )
    _a					:  Any						=  outputs[0]

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




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Dict						=  self.hparams
    _a					:  int						=  processors[args.task]()
    _a					:  List[str]						=  processor.get_labels()

    for mode in ["train", "dev"]:
       _a					:  Union[str, Any]						=  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      )
          _a					:  List[str]						=  (
              processor.get_dev_examples(args.data_dir      )
              if mode == 'dev'
              else processor.get_train_examples(args.data_dir      )
          )
          _a					:  Tuple						=  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		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = False      )      ->    DataLoader:
    _a					:  Dict						=  'dev' if mode == 'test' else mode

    _a					:  Optional[Any]						=  self._feature_file(lowerCamelCase_      )
    logger.info('Loading features from cached file %s'		,      lowerCamelCase_      )
    _a					:  Optional[Any]						=  torch.load(lowerCamelCase_      )
    _a					:  int						=  torch.tensor([f.input_ids for f in features]		,      dtype=torch.long      )
    _a					:  List[str]						=  torch.tensor([f.attention_mask for f in features]		,      dtype=torch.long      )
    _a					:  List[str]						=  torch.tensor([f.token_type_ids for f in features]		,      dtype=torch.long      )
    if self.hparams.glue_output_mode == "classification":
       _a					:  str						=  torch.tensor([f.label for f in features]		,      dtype=torch.long      )
    elif self.hparams.glue_output_mode == "regression":
       _a					:  List[str]						=  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		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    _a					:  List[str]						=  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

    _a					:  int						=  self(**lowerCamelCase_      )
    _a		,						_a					:  List[Any]						=  outputs[:2]
    _a					:  List[str]						=  logits.detach().cpu().numpy()
    _a					:  List[Any]						=  inputs['labels'].detach().cpu().numpy()

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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    tuple:
    _a					:  int						=  torch.stack([x['val_loss'] for x in outputs]      ).mean().detach().cpu().item()
    _a					:  Any						=  np.concatenate([x['pred'] for x in outputs]		,      axis=0      )

    if self.hparams.glue_output_mode == "classification":
       _a					:  Any						=  np.argmax(lowerCamelCase_		,      axis=1      )
    elif self.hparams.glue_output_mode == "regression":
       _a					:  Tuple						=  np.squeeze(lowerCamelCase_      )

    _a					:  Dict						=  np.concatenate([x['target'] for x in outputs]		,      axis=0      )
    _a					:  Any						=  [[] for _ in range(out_label_ids.shape[0]      )]
    _a					:  List[Any]						=  [[] for _ in range(out_label_ids.shape[0]      )]

    _a					:  Union[str, Any]						=  {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,      lowerCamelCase_		,      lowerCamelCase_      )}

    _a					:  Optional[int]						=  dict(results.items()      )
    _a					:  Tuple						=  results
    return ret, preds_list, out_label_list




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    dict:
    _a		,						_a		,						_a					:  Tuple						=  self._eval_end(lowerCamelCase_      )
    _a					:  List[str]						=  ret['log']
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    dict:
    _a		,						_a		,						_a					:  Tuple						=  self._eval_end(lowerCamelCase_      )
    _a					:  Dict						=  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   (    lowerCamelCase_		,      lowerCamelCase_      )      ->    Tuple:
    BaseTransformer.add_model_specific_args(lowerCamelCase_		,      lowerCamelCase_      )
    parser.add_argument(
        '--max_seq_length'		,      default=1_2_8		,      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_  (       ):
   '''simple docstring'''





   _a					:  List[str]						=  argparse.ArgumentParser()
   add_generic_args(A		,	os.getcwd()      )
   _a					:  Optional[Any]						=  GLUETransformer.add_model_specific_args(A		,	os.getcwd()      )
   _a					:  Optional[Any]						=  parser.parse_args()

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

   _a					:  Any						=  GLUETransformer(A      )
   _a					:  int						=  generic_train(A		,	A      )

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


if __name__ == "__main__":
 main()






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      :		Optional[int]          =		logging.get_logger(__name__)

UpperCAmelCase_      :		List[str]          =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: str							  =		"""vivit"""




 def __init__(    self		,      lowerCamelCase_=2_2_4		,      lowerCamelCase_=3_2		,      lowerCamelCase_=[2, 1_6, 1_6]		,      lowerCamelCase_=3		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_="gelu_fast"		,      lowerCamelCase_=0.0		,      lowerCamelCase_=0.0		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-06		,      lowerCamelCase_=True		,      **lowerCamelCase_		,      )      ->    int:
    _a					:  Tuple						=  hidden_size
    _a					:  Dict						=  num_hidden_layers
    _a					:  List[str]						=  num_attention_heads
    _a					:  int						=  intermediate_size
    _a					:  Optional[int]						=  hidden_act
    _a					:  Dict						=  hidden_dropout_prob
    _a					:  List[str]						=  attention_probs_dropout_prob
    _a					:  List[str]						=  initializer_range
    _a					:  List[str]						=  layer_norm_eps

    _a					:  Any						=  image_size
    _a					:  Optional[Any]						=  num_frames
    _a					:  Dict						=  tubelet_size
    _a					:  Union[str, Any]						=  num_channels
    _a					:  Optional[int]						=  qkv_bias

    super().__init__(**lowerCamelCase_      )


