



'''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


lowerCAmelCase:      Any		 =		logging.getLogger(__name__)

class 							a__(      lowerCamelCase__			):
    lowercase__				=					"""sequence-classification"""







    def __init__(   self				:						Any		,    __snake_case				:						Tuple			):
      if type(__snake_case			) == dict:
        a		:      Any       =  Namespace(**__snake_case			)
      a		:      Optional[Any]       =  glue_output_modes[hparams.task]
      a		:      str       =  glue_tasks_num_labels[hparams.task]

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







    def     lowercase_ (   self				:						str		,    **__snake_case				:						Optional[Any]			):
      return self.model(**__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str		,    __snake_case				:						Union[str, Any]			):
      a		:      Any       =  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

      a		:      Any       =  self(**__snake_case			)
      a		:      Optional[Any]       =  outputs[0]

      a		:      Dict       =  self.trainer.lr_schedulers[0]['scheduler']
      a		:      Tuple       =  {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
      return {"loss": loss, "log": tensorboard_logs}







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Tuple       =  self.hparams
      a		:      Dict       =  processors[args.task]()
      a		:      int       =  processor.get_labels()

      for mode in ["train", "dev"]:
        a		:      Any       =  self._feature_file(__snake_case			)
        if os.path.exists(__snake_case			) and not args.overwrite_cache:
          logger.info('Loading features from cached file %s'		,    __snake_case			)
        else:
          logger.info('Creating features from dataset file at %s'		,    args.data_dir			)
          a		:      int       =  (
              processor.get_dev_examples(args.data_dir			)
              if mode == 'dev'
              else processor.get_train_examples(args.data_dir			)
          )
          a		:      Union[str, Any]       =  convert_examples_to_features(
              __snake_case		,    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'		,    __snake_case			)
          torch.save(__snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						str		,    __snake_case				:						int		,    __snake_case				:						bool = False			):
      a		:      List[Any]       =  'dev' if mode == 'test' else mode

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

      return DataLoader(
          TensorDataset(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)		,    batch_size=__snake_case		,    shuffle=__snake_case		,    )







    def     lowercase_ (   self				:						Any		,    __snake_case				:						Tuple		,    __snake_case				:						List[Any]			):
      a		:      List[str]       =  {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

      a		:      Any       =  self(**__snake_case			)
      a     ,			a		:      str       =  outputs[:2]
      a		:      List[str]       =  logits.detach().cpu().numpy()
      a		:      str       =  inputs['labels'].detach().cpu().numpy()

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







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[Any]			):
      a		:      Optional[int]       =  torch.stack([x['val_loss'] for x in outputs]			).mean().detach().cpu().item()
      a		:      Dict       =  np.concatenate([x['pred'] for x in outputs]		,    axis=0			)

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

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

      a		:      List[str]       =  {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,    __snake_case		,    __snake_case			)}

      a		:      Optional[int]       =  dict(results.items()			)
      a		:      Tuple       =  results
      return ret, preds_list, out_label_list







    def     lowercase_ (   self				:						Any		,    __snake_case				:						list			):
      a     ,			a     ,			a		:      int       =  self._eval_end(__snake_case			)
      a		:      int       =  ret['log']
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]			):
      a     ,			a     ,			a		:      Dict       =  self._eval_end(__snake_case			)
      a		:      int       =  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     lowercase_ (   __snake_case				:						str		,    __snake_case				:						List[Any]			):
      BaseTransformer.add_model_specific_args(__snake_case		,    __snake_case			)
      parser.add_argument(
          '--max_seq_length'		,    default=1_28		,    type=__snake_case		,    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=__snake_case		,    required=__snake_case		,    help='The GLUE task to run'		,    )
      parser.add_argument(
          '--gpus'		,    default=0		,    type=__snake_case		,    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__ (		):
  a		:      Any       =  argparse.ArgumentParser()
  add_generic_args(_A				,    os.getcwd()       )
  a		:      Tuple       =  GLUETransformer.add_model_specific_args(_A				,    os.getcwd()       )
  a		:      Tuple       =  parser.parse_args()

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

  a		:      Optional[Any]       =  GLUETransformer(_A       )
  a		:      Union[str, Any]       =  generic_train(_A				,    _A       )

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


if __name__ == "__main__":
     main()




'''simple docstring'''






from math import factorial, pi
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'       )

  a		:      List[Any]       =  float(_A       )
  a		:      Optional[int]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1       ) for r in range(_A       )       )
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'       )

  a		:      List[str]       =  float(_A       )
  a		:      List[str]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r       ) for r in range(_A       )       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))