


"""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


__snake_case		=       logging.getLogger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[str]    =		'sequence-classification'


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

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


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							List[Any]:
          return self.model(**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

          _a    	=				self(**__UpperCAmelCase							)
          _a    	=				outputs[0]

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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.hparams
          _a    	=				processors[args.task]()
          _a    	=				processor.get_labels()

          for mode in ["train", "dev"]:
               _a    	=				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							)
                    _a    	=				(
                        processor.get_dev_examples(args.data_dir							)
                        if mode == '''dev'''
                        else processor.get_train_examples(args.data_dir							)
                    )
                    _a    	=				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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = False							)       ->							DataLoader:
          _a    	=				'''dev''' if mode == '''test''' else mode

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

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							int:
          _a    	=				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

          _a    	=				self(**__UpperCAmelCase							)
          _a	,      _a    	=				outputs[:2]
          _a    	=				logits.detach().cpu().numpy()
          _a    	=				inputs['''labels'''].detach().cpu().numpy()

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


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

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

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

          _a    	=				{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)}

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							dict:
          _a	,      _a	,      _a    	=				self._eval_end(__UpperCAmelCase							)
          _a    	=				ret['''log''']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							dict:
          _a	,      _a	,      _a    	=				self._eval_end(__UpperCAmelCase							)
          _a    	=				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      (      __UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          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_   (   ):






     """simple docstring"""

     _a    	=				argparse.ArgumentParser()
     add_generic_args(_lowerCAmelCase,     os.getcwd()       )
     _a    	=				GLUETransformer.add_model_specific_args(_lowerCAmelCase,     os.getcwd()       )
     _a    	=				parser.parse_args()

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

     _a    	=				GLUETransformer(_lowerCAmelCase       )
     _a    	=				generic_train(_lowerCAmelCase,     _lowerCAmelCase       )

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


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_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

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