




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


lowerCamelCase       :       Optional[Any]        =    logging.getLogger(__name__)




class 						__lowercase		(_UpperCAmelCase							):




      """simple docstring"""





      _snake_case							     =						"""sequence-classification"""

      def __init__(      self				,      A	)		->				Optional[Any]:
         if type(A	) == dict:
            snake_case      :				Tuple				=       Namespace(**A	)
         snake_case      :				Dict				=       glue_output_modes[hparams.task]
         snake_case      :				int				=       glue_tasks_num_labels[hparams.task]

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

      def 	UpperCAmelCase      (      self				,      **A	)		->				List[str]:
         return self.model(**A	)

      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				List[str]:
         snake_case      :				Optional[Any]				=       {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

         snake_case      :				str				=       self(**A	)
         snake_case      :				Union[str, Any]				=       outputs[0]

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

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

         for mode in ["train", "dev"]:
            snake_case      :				Tuple				=       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	)
               snake_case      :				int				=       (
                   processor.get_dev_examples(args.data_dir	)
                   if mode == """dev"""
                   else processor.get_train_examples(args.data_dir	)
               )
               snake_case      :				Union[str, Any]				=       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				,      A				,      A				,      A = False	)		->				DataLoader:
         snake_case      :				Tuple				=       """dev""" if mode == """test""" else mode

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

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

         snake_case      :				Optional[int]				=       self(**A	)
         snake_case	,					snake_case      :				List[Any]				=       outputs[:2]
         snake_case      :				List[str]				=       logits.detach().cpu().numpy()
         snake_case      :				Optional[Any]				=       inputs["""labels"""].detach().cpu().numpy()

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

      def 	UpperCAmelCase      (      self				,      A	)		->				tuple:
         snake_case      :				Union[str, Any]				=       torch.stack([x["""val_loss"""] for x in outputs]	).mean().detach().cpu().item()
         snake_case      :				str				=       np.concatenate([x["""pred"""] for x in outputs]				,      axis=0	)

         if self.hparams.glue_output_mode == "classification":
            snake_case      :				Optional[Any]				=       np.argmax(A				,      axis=1	)
         elif self.hparams.glue_output_mode == "regression":
            snake_case      :				List[Any]				=       np.squeeze(A	)

         snake_case      :				Tuple				=       np.concatenate([x["""target"""] for x in outputs]				,      axis=0	)
         snake_case      :				List[Any]				=       [[] for _ in range(out_label_ids.shape[0]	)]
         snake_case      :				Optional[int]				=       [[] for _ in range(out_label_ids.shape[0]	)]

         snake_case      :				Any				=       {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task				,      A				,      A	)}

         snake_case      :				str				=       dict(results.items()	)
         snake_case      :				int				=       results
         return ret, preds_list, out_label_list

      def 	UpperCAmelCase      (      self				,      A	)		->				dict:
         snake_case	,					snake_case	,					snake_case      :				List[str]				=       self._eval_end(A	)
         snake_case      :				Any				=       ret["""log"""]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

      def 	UpperCAmelCase      (      self				,      A	)		->				dict:
         snake_case	,					snake_case	,					snake_case      :				List[Any]				=       self._eval_end(A	)
         snake_case      :				List[str]				=       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      (      A				,      A	)		->				Optional[Any]:
         BaseTransformer.add_model_specific_args(A				,      A	)
         parser.add_argument(
             """--max_seq_length"""				,      default=1_2_8				,      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 						SCREAMING_SNAKE_CASE__					(    )  ->   Optional[int]:
   snake_case      :				List[Any]				=       argparse.ArgumentParser()
   add_generic_args(lowercase						,os.getcwd()     )
   snake_case      :				Tuple				=       GLUETransformer.add_model_specific_args(lowercase						,os.getcwd()     )
   snake_case      :				List[Any]				=       parser.parse_args()

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

   snake_case      :				Union[str, Any]				=       GLUETransformer(lowercase     )
   snake_case      :				List[str]				=       generic_train(lowercase						,lowercase     )

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


if __name__ == "__main__":
       main()












"""simple docstring"""





from __future__ import annotations


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return [ord(SCREAMING_SNAKE_CASE			) - 96 for elem in plain]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      return "".join(chr(elem + 96			) for elem in encoded			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       encode(input("""-> """			).strip().lower()			)
      print("""Encoded: """							,						SCREAMING_SNAKE_CASE			)
      print("""Decoded:"""							,						decode(SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
       main()






