






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


__UpperCamelCase      :      str     						=						logging.getLogger(__name__)


class        __UpperCamelCase    (     __lowercase   ):
	__snake_case	:int   =       'sequence-classification'







	def __init__(  self      :							List[Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		if type(__A			) == dict:
			__lowercase             =  Namespace(**__A			)
		__lowercase             =  glue_output_modes[hparams.task]
		__lowercase             =  glue_tasks_num_labels[hparams.task]

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







	def  _a   (  self      :							int       ,				**_lowerCAmelCase      :							Dict			)		-> Any:
		"""simple docstring"""







		return self.model(**__A			)







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Any			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

		__lowercase             =  self(**__A			)
		__lowercase             =  outputs[0]

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







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.hparams
		__lowercase             =  processors[args.task]()
		__lowercase             =  processor.get_labels()

		for mode in ["train", "dev"]:
			__lowercase             =  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			)
				__lowercase             =  (
				    processor.get_dev_examples(args.data_dir			)
				    if mode == """dev"""
				    else processor.get_train_examples(args.data_dir			)
				)
				__lowercase             =  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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Any = False			)		-> DataLoader:
		"""simple docstring"""







		__lowercase             =  """dev""" if mode == """test""" else mode

		__lowercase             =  self._feature_file(__A			)
		logger.info("""Loading features from cached file %s"""       ,				__A			)
		__lowercase             =  torch.load(__A			)
		__lowercase             =  torch.tensor([f.input_ids for f in features]       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor([f.attention_mask for f in features]       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor([f.token_type_ids for f in features]       ,				dtype=torch.long			)
		if self.hparams.glue_output_mode == "classification":
			__lowercase             =  torch.tensor([f.label for f in features]       ,				dtype=torch.long			)
		elif self.hparams.glue_output_mode == "regression":
			__lowercase             =  torch.tensor([f.label for f in features]       ,				dtype=torch.float			)

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







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

		__lowercase             =  self(**__A			)
		__lowercase       ,						__lowercase             =  outputs[:2]
		__lowercase             =  logits.detach().cpu().numpy()
		__lowercase             =  inputs["""labels"""].detach().cpu().numpy()

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







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							List[str]			)		-> tuple:
		"""simple docstring"""







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

		if self.hparams.glue_output_mode == "classification":
			__lowercase             =  np.argmax(__A       ,				axis=1			)
		elif self.hparams.glue_output_mode == "regression":
			__lowercase             =  np.squeeze(__A			)

		__lowercase             =  np.concatenate([x["""target"""] for x in outputs]       ,				axis=0			)
		__lowercase             =  [[] for _ in range(out_label_ids.shape[0]			)]
		__lowercase             =  [[] for _ in range(out_label_ids.shape[0]			)]

		__lowercase             =  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task       ,				__A       ,				__A			)}

		__lowercase             =  dict(results.items()			)
		__lowercase             =  results
		return ret, preds_list, out_label_list







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Tuple			)		-> dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase             =  self._eval_end(__A			)
		__lowercase             =  ret["""log"""]
		return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Union[str, Any]			)		-> dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase             =  self._eval_end(__A			)
		__lowercase             =  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  _a   (  _lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							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 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser()
	add_generic_args(a__				,  os.getcwd()					)
	__lowercase             =  GLUETransformer.add_model_specific_args(a__				,  os.getcwd()					)
	__lowercase             =  parser.parse_args()

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

	__lowercase             =  GLUETransformer(a__					)
	__lowercase             =  generic_train(a__				,  a__					)

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


if __name__ == "__main__":
						main()












from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

