




"""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


A_						=				logging.getLogger(__name__)






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sequence-classification"


 def __init__(				self:		Tuple,      a_:		List[Any]		):




   '''simple docstring'''



   if type(a_		) == dict:
     _snake_case							:		List[Any]								=  Namespace(**a_		)
   _snake_case							:		str								=  glue_output_modes[hparams.task]
   _snake_case							:		int								=  glue_tasks_num_labels[hparams.task]

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


 def  UpperCamelCase_   (				self:		Tuple,      **a_:		Optional[Any]		):




   '''simple docstring'''



   return self.model(**a_		)


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		str,      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  {"""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							:		Any								=  self(**a_		)
   _snake_case							:		Any								=  outputs[0]

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


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		str								=  self.hparams
   _snake_case							:		int								=  processors[args.task]()
   _snake_case							:		Tuple								=  processor.get_labels()

   for mode in ["train", "dev"]:
     _snake_case							:		Union[str, Any]								=  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							:		Dict								=  (
           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:		Any,      a_:		str,      a_:		int,      a_:		bool = False		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """dev""" if mode == """test""" else mode

   _snake_case							:		Optional[int]								=  self._feature_file(a_		)
   logger.info("""Loading features from cached file %s""",      a_		)
   _snake_case							:		List[Any]								=  torch.load(a_		)
   _snake_case							:		Optional[int]								=  torch.tensor([f.input_ids for f in features],      dtype=torch.long		)
   _snake_case							:		Union[str, Any]								=  torch.tensor([f.attention_mask for f in features],      dtype=torch.long		)
   _snake_case							:		Optional[Any]								=  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							:		List[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:		Union[str, Any],      a_:		Tuple,      a_:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  {"""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							:		int								=  self(**a_		)
   _snake_case		,					_snake_case							:		str								=  outputs[:2]
   _snake_case							:		Tuple								=  logits.detach().cpu().numpy()
   _snake_case							:		Tuple								=  inputs["""labels"""].detach().cpu().numpy()

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


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Any								=  torch.stack([x["""val_loss"""] for x in outputs]		).mean().detach().cpu().item()
   _snake_case							:		Optional[int]								=  np.concatenate([x["""pred"""] for x in outputs],      axis=0		)

   if self.hparams.glue_output_mode == "classification":
     _snake_case							:		Tuple								=  np.argmax(a_,      axis=1		)
   elif self.hparams.glue_output_mode == "regression":
     _snake_case							:		Optional[Any]								=  np.squeeze(a_		)

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

   _snake_case							:		str								=  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task,      a_,      a_		)}

   _snake_case							:		str								=  dict(results.items()		)
   _snake_case							:		Optional[Any]								=  results
   return ret, preds_list, out_label_list


 def  UpperCamelCase_   (				self:		str,      a_:		list		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case							:		List[Any]								=  self._eval_end(a_		)
   _snake_case							:		Any								=  ret["""log"""]
   return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		str		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  self._eval_end(a_		)
   _snake_case							:		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_:		Optional[int],      a_:		List[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 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  argparse.ArgumentParser()
  add_generic_args(snake_case__		,   os.getcwd()    )
  _snake_case							:		Any								=  GLUETransformer.add_model_specific_args(snake_case__		,   os.getcwd()    )
  _snake_case							:		Optional[Any]								=  parser.parse_args()

  # If output_dir not provided, a folder will be generated in pwd
  if args.output_dir is None:
    _snake_case							:		Optional[int]								=  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(snake_case__    )
  _snake_case							:		Dict								=  generic_train(snake_case__		,   snake_case__    )

  # 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=snake_case__    )    )
    _snake_case							:		Union[str, Any]								=  model.load_from_checkpoint(checkpoints[-1]    )
    return trainer.test(snake_case__    )


if __name__ == "__main__":
     main()








"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		int								=  0.00
  _snake_case							:		int								=  0
  for resistor in resistors:
    if resistor <= 0:
      _snake_case							:		Dict								=  F"Resistor at index {index} has a negative or zero value!"
      raise ValueError(snake_case__    )
    first_sum += 1 / float(snake_case__    )
    index += 1
  return 1 / first_sum


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  0.00
  _snake_case							:		Any								=  0
  for resistor in resistors:
    sum_r += resistor
    if resistor < 0:
      _snake_case							:		Any								=  F"Resistor at index {index} has a negative value!"
      raise ValueError(snake_case__    )
    index += 1
  return sum_r


if __name__ == "__main__":
     import doctest

     doctest.testmod()


