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


a_							=      logging.getLogger(__name__)


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= 'sequence-classification'






  def __init__(				self:			str				,				__lowerCAmelCase:			Union[str, Any]   ) -> List[str]:




    '''simple docstring'''




    if type(__A   ) == dict:
      __UpperCAmelCase						    =       Namespace(**__A   )
    __UpperCAmelCase						    =       glue_output_modes[hparams.task]
    __UpperCAmelCase						    =       glue_tasks_num_labels[hparams.task]

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






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				**__lowerCAmelCase:			Union[str, Any]   ) -> Optional[int]:




    '''simple docstring'''




    return self.model(**__A   )






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			int   ) -> Optional[Any]:




    '''simple docstring'''




    __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(**__A   )
    __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 	_UpperCAmelCase				(				self:			List[str]   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.hparams
    __UpperCAmelCase						    =       processors[args.task]()
    __UpperCAmelCase						    =       processor.get_labels()

    for mode in ["train", "dev"]:
      __UpperCAmelCase						    =       self._feature_file(__A   )
      if os.path.exists(__A   ) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s"				,				__A   )
      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(
            __A				,				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"				,				__A   )
        torch.save(__A				,				__A   )






  def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			bool = False   ) -> DataLoader:




    '''simple docstring'''




    __UpperCAmelCase						    =       "dev" if mode == "test" else mode

    __UpperCAmelCase						    =       self._feature_file(__A   )
    logger.info("Loading features from cached file %s"				,				__A   )
    __UpperCAmelCase						    =       torch.load(__A   )
    __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(__A				,				__A				,				__A				,				__A   )				,				batch_size=__A				,				shuffle=__A				,				)






  def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Tuple   ) -> str:




    '''simple docstring'''




    __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(**__A   )
    __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 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			Dict   ) -> tuple:




    '''simple docstring'''




    __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(__A				,				axis=1   )
    elif self.hparams.glue_output_mode == "regression":
      __UpperCAmelCase						    =       np.squeeze(__A   )

    __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				,				__A				,				__A   )}

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






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			list   ) -> dict:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       self._eval_end(__A   )
    __UpperCAmelCase						    =       ret["log"]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Union[str, Any]   ) -> dict:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       self._eval_end(__A   )
    __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 	_UpperCAmelCase				(				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			Optional[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    BaseTransformer.add_model_specific_args(__A				,				__A   )
    parser.add_argument(
        "--max_seq_length"				,				default=128				,				type=__A				,				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=__A				,				required=__A				,				help="The GLUE task to run"				,				)
    parser.add_argument(
        "--gpus"				,				default=0				,				type=__A				,				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       __lowerCAmelCase      (						)    ->  Tuple:
  __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()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

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



