




"""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:		Optional[int],				a_:		Optional[Any]		):




   '''simple docstring'''



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

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


 def 			UpperCamelCase_       (    self:		Dict,				**a_:		Union[str, Any]		):




   '''simple docstring'''



   return self.model(**a_		)


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




   '''simple docstring'''



   _snake_case							:		List[Any]								=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

   _snake_case							:		Union[str, Any]								=  self(**a_		)
   _snake_case							:		List[str]								=  outputs[0]

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


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



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

   for mode in ["train", "dev"]:
     _snake_case							:		List[str]								=  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							:		Tuple								=  (
           processor.get_dev_examples(args.data_dir		)
           if mode == """dev"""
           else processor.get_train_examples(args.data_dir		)
       )
       _snake_case							:		List[str]								=  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:		int,				a_:		str,				a_:		int,				a_:		bool = False		):




   '''simple docstring'''



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

   _snake_case							:		int								=  self._feature_file(a_		)
   logger.info("""Loading features from cached file %s""",				a_		)
   _snake_case							:		Union[str, Any]								=  torch.load(a_		)
   _snake_case							:		List[Any]								=  torch.tensor([f.input_ids for f in features],				dtype=torch.long		)
   _snake_case							:		Dict								=  torch.tensor([f.attention_mask for f in features],				dtype=torch.long		)
   _snake_case							:		int								=  torch.tensor([f.token_type_ids for f in features],				dtype=torch.long		)
   if self.hparams.glue_output_mode == "classification":
     _snake_case							:		Dict								=  torch.tensor([f.label for f in features],				dtype=torch.long		)
   elif self.hparams.glue_output_mode == "regression":
     _snake_case							:		Union[str, Any]								=  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:		str,				a_:		Union[str, Any],				a_:		Optional[int]		):




   '''simple docstring'''



   _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							:		List[str]								=  batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

   _snake_case							:		List[Any]								=  self(**a_		)
   _snake_case		, _snake_case							:		List[str]								=  outputs[:2]
   _snake_case							:		Dict								=  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:		Dict,				a_:		int		):




   '''simple docstring'''



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

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

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

   _snake_case							:		List[Any]								=  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task,				a_,				a_		)}

   _snake_case							:		int								=  dict(results.items()		)
   _snake_case							:		Any								=  results
   return ret, preds_list, out_label_list


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




   '''simple docstring'''



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


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




   '''simple docstring'''



   _snake_case		, _snake_case		, _snake_case							:		List[str]								=  self._eval_end(a_		)
   _snake_case							:		List[Any]								=  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_:		List[str],				a_:		Union[str, 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							:		int								=  argparse.ArgumentParser()
  add_generic_args(snake_case__		,       os.getcwd()   )
  _snake_case							:		str								=  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							:		str								=  os.path.join(
        """./results"""		,       F"{args.task}_{time.strftime('%Y%m%d_%H%M%S'   )}"		,       )
    os.makedirs(args.output_dir   )

  _snake_case							:		str								=  GLUETransformer(snake_case__   )
  _snake_case							:		int								=  generic_train(snake_case__		,       snake_case__   )

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


if __name__ == "__main__":
     main()








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


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


