


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__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''sequence-classification'''





     def __init__(self		,     UpperCAmelCase       )     ->   Union[str, Any]:
      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      __A					(self		,     **UpperCAmelCase       )     ->   List[Any]:
      return self.model(**UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _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      __A					(self       )     ->   Dict:
      _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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = False       )     ->   DataLoader:
      _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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   int:
      _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      ,    _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      __A					(self		,     UpperCAmelCase       )     ->   tuple:
      _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      __A					(self		,     UpperCAmelCase       )     ->   dict:
      _lowercase      ,    _lowercase      ,    _lowercase							      =self._eval_end(UpperCAmelCase       )
      _lowercase							      =ret['''log''']
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





     def      __A					(self		,     UpperCAmelCase       )     ->   dict:
      _lowercase      ,    _lowercase      ,    _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      __A					(UpperCAmelCase		,     UpperCAmelCase       )     ->   List[Any]:
      BaseTransformer.add_model_specific_args(UpperCAmelCase		,     UpperCAmelCase       )
      parser.add_argument(
          '''--max_seq_length'''		,     default=1_2_8		,     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_					(							)     ->			Dict:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser()
 add_generic_args(__snake_case     ,						os.getcwd()						)
 _lowercase							      =GLUETransformer.add_model_specific_args(__snake_case     ,						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(__snake_case						)
 _lowercase							      =generic_train(__snake_case     ,						__snake_case						)

 # 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=__snake_case						)						)
  _lowercase							      =model.load_from_checkpoint(checkpoints[-1]						)
  return trainer.test(__snake_case						)


if __name__ == "__main__":
  main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

