


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			:						Optional[Any]        =  logging.getLogger(__name__)



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''sequence-classification'''

					def __init__(self       :		str ,   _UpperCAmelCase       :		int						)		->  Optional[int]:




							"""simple docstring"""


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

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

					def     lowerCamelCase__	(self       :		Optional[int] ,   **_UpperCAmelCase       :		Optional[Any]						)		->  Dict:




							"""simple docstring"""


							return self.model(**_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		List[Any]						)		->  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(**_UpperCAmelCase						)
							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     lowerCamelCase__	(self       :		Optional[Any]						)		->  Dict:




							"""simple docstring"""


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

							for mode in ["train", "dev"]:
									lowercase__	     =						self._feature_file(_UpperCAmelCase						)
									if os.path.exists(_UpperCAmelCase						) and not args.overwrite_cache:
											logger.info("""Loading features from cached file %s""" ,   _UpperCAmelCase						)
									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(
											    _UpperCAmelCase ,   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""" ,   _UpperCAmelCase						)
											torch.save(_UpperCAmelCase ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		bool = False						)		->  DataLoader:




							"""simple docstring"""


							lowercase__	     =						"""dev""" if mode == """test""" else mode

							lowercase__	     =						self._feature_file(_UpperCAmelCase						)
							logger.info("""Loading features from cached file %s""" ,   _UpperCAmelCase						)
							lowercase__	     =						torch.load(_UpperCAmelCase						)
							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(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						) ,   batch_size=_UpperCAmelCase ,   shuffle=_UpperCAmelCase ,   )

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Dict						)		->  int:




							"""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(**_UpperCAmelCase						)
							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     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Any						)		->  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(_UpperCAmelCase ,   axis=1						)
							elif self.hparams.glue_output_mode == "regression":
									lowercase__	     =						np.squeeze(_UpperCAmelCase						)

							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 ,   _UpperCAmelCase ,   _UpperCAmelCase						)}

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

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		list						)		->  dict:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any]						)		->  dict:




							"""simple docstring"""


							lowercase__     ,      lowercase__     ,      lowercase__	     =						self._eval_end(_UpperCAmelCase						)
							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     lowerCamelCase__	(_UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Any						)		->  Optional[Any]:




							"""simple docstring"""


							BaseTransformer.add_model_specific_args(_UpperCAmelCase ,   _UpperCAmelCase						)
							parser.add_argument(
							    """--max_seq_length""" ,   default=128 ,   type=_UpperCAmelCase ,   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=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help="""The GLUE task to run""" ,   )
							parser.add_argument(
							    """--gpus""" ,   default=0 ,   type=_UpperCAmelCase ,   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   (      )  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser()
		add_generic_args(__magic_name__       ,       os.getcwd()	)
		lowercase__	     =						GLUETransformer.add_model_specific_args(__magic_name__       ,       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(__magic_name__	)
		lowercase__	     =						generic_train(__magic_name__       ,       __magic_name__	)

		# 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=__magic_name__	)	)
				lowercase__	     =						model.load_from_checkpoint(checkpoints[-1]	)
				return trainer.test(__magic_name__	)


if __name__ == "__main__":
	main()





from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

