"""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 							a					(		lowerCAmelCase_ ):
    _snake_case  :	str  				=				'sequence-classification'


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      if type(__lowerCAmelCase       ) == dict:
        _UpperCAmelCase    			=   Namespace(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   glue_output_modes[hparams.task]
      _UpperCAmelCase    			=   glue_tasks_num_labels[hparams.task]

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


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					**__lowerCAmelCase						:	Tuple       ):
      return self.model(**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

      _UpperCAmelCase    			=   self(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   outputs[0]

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


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.hparams
      _UpperCAmelCase    			=   processors[args.task]()
      _UpperCAmelCase    			=   processor.get_labels()

      for mode in ["train", "dev"]:
        _UpperCAmelCase    			=   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       )
          _UpperCAmelCase    			=   (
              processor.get_dev_examples(args.data_dir       )
              if mode == """dev"""
              else processor.get_train_examples(args.data_dir       )
          )
          _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	bool = False       ):
      _UpperCAmelCase    			=   """dev""" if mode == """test""" else mode

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

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


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

      _UpperCAmelCase    			=   self(**__lowerCAmelCase       )
      _UpperCAmelCase			,					_UpperCAmelCase    			=   outputs[:2]
      _UpperCAmelCase    			=   logits.detach().cpu().numpy()
      _UpperCAmelCase    			=   inputs["""labels"""].detach().cpu().numpy()

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


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   torch.stack([x["""val_loss"""] for x in outputs]       ).mean().detach().cpu().item()
      _UpperCAmelCase    			=   np.concatenate([x["""pred"""] for x in outputs] ,					axis=0       )

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

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

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

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


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	list       ):
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   self._eval_end(__lowerCAmelCase       )
      _UpperCAmelCase    			=   ret["""log"""]
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   self._eval_end(__lowerCAmelCase       )
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       __lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict       ):
      BaseTransformer.add_model_specific_args(__lowerCAmelCase ,					__lowerCAmelCase       )
      parser.add_argument(
          """--max_seq_length""" ,					default=128 ,					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"""
  _UpperCAmelCase    			=   argparse.ArgumentParser()
  add_generic_args(lowercase ,os.getcwd()		)
  _UpperCAmelCase    			=   GLUETransformer.add_model_specific_args(lowercase ,os.getcwd()		)
  _UpperCAmelCase    			=   parser.parse_args()

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

  _UpperCAmelCase    			=   GLUETransformer(lowercase		)
  _UpperCAmelCase    			=   generic_train(lowercase ,lowercase		)

  # Optionally, predict on dev set and write to output_dir
  if args.do_predict:
    _UpperCAmelCase    			=   sorted(glob.glob(os.path.join(args.output_dir ,"""checkpoint-epoch=*.ckpt"""		) ,recursive=lowercase		)		)
    _UpperCAmelCase    			=   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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

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