




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	:	Tuple				=   logging.getLogger(__name__)




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""sequence-classification"""

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

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

      def      UpperCAmelCase				(				self    ,				**A     )		->    Any:
         return self.model(**A     )

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

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

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

         snake_case				:    List[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     )		->    Optional[int]:
         snake_case				:    Optional[int]           =	self.hparams
         snake_case				:    Optional[Any]           =	processors[args.task]()
         snake_case				:    str           =	processor.get_labels()

         for mode in ["train", "dev"]:
            snake_case				:    int           =	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				:    Optional[int]           =	(
                   processor.get_dev_examples(args.data_dir     )
                   if mode == """dev"""
                   else processor.get_train_examples(args.data_dir     )
               )
               snake_case				:    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				:    Optional[Any]           =	"""dev""" if mode == """test""" else mode

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

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

         snake_case				:    Dict           =	self(**A     )
         snake_case     , snake_case				:    Tuple           =	outputs[:2]
         snake_case				:    Optional[Any]           =	logits.detach().cpu().numpy()
         snake_case				:    Union[str, 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				:    Optional[Any]           =	torch.stack([x["""val_loss"""] for x in outputs]     ).mean().detach().cpu().item()
         snake_case				:    Any           =	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				:    Dict           =	np.squeeze(A     )

         snake_case				:    Optional[Any]           =	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[Any]           =	[[] 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				:    Dict           =	dict(results.items()     )
         snake_case				:    Tuple           =	results
         return ret, preds_list, out_label_list

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

      def      UpperCAmelCase				(				self    ,				A     )		->    dict:
         snake_case     , snake_case     , snake_case				:    Optional[int]           =	self._eval_end(A     )
         snake_case				:    Tuple           =	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[int]:
         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__ (   )  ->       str:
   snake_case				:    str           =	argparse.ArgumentParser()
   add_generic_args(lowercase						,os.getcwd()					)
   snake_case				:    str           =	GLUETransformer.add_model_specific_args(lowercase						,os.getcwd()					)
   snake_case				:    Dict           =	parser.parse_args()

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

   snake_case				:    Any           =	GLUETransformer(lowercase					)
   snake_case				:    Any           =	generic_train(lowercase						,lowercase					)

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


if __name__ == "__main__":
       main()











def 						SCREAMING_SNAKE_CASE__ (   lowercase = 1000					)  ->       int:
   snake_case				:    Optional[int]           =	3
   snake_case				:    List[Any]           =	0
   while a < n:
      if a % 3 == 0 or a % 5 == 0:
         result += a
      elif a % 15 == 0:
         result -= a
      a += 1
   return result


if __name__ == "__main__":
       print(f"""{solution() = }""")





