





"""simple docstring"""






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A 			=   logging.getLogger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''token-classification'''







					def __init__(						self	,			_UpperCAmelCase     ):
									if type(_UpperCAmelCase     ) == dict:
													__a :						Union[str, Any]			       =  Namespace(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  import_module('''tasks'''     )
									try:
													__a :						Any			       =  getattr(_UpperCAmelCase	,			hparams.task_type     )
													__a :						TokenClassificationTask			       =  token_classification_task_clazz()
									except AttributeError:
													raise ValueError(
													    f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
													    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""     )
									__a :						Optional[Any]			       =  self.token_classification_task.get_labels(hparams.labels     )
									__a :						int			       =  CrossEntropyLoss().ignore_index
									super().__init__(_UpperCAmelCase	,			len(self.labels     )	,			self.mode     )







					def  _lowerCamelCase		(						self	,			**_UpperCAmelCase     ):
									return self.model(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
									if self.config.model_type != "distilbert":
													__a :						Optional[int]			       =  (
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids

									__a :						Union[str, Any]			       =  self(**_UpperCAmelCase     )
									__a :						List[Any]			       =  outputs[0]
									# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
									return {"loss": loss}







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  self.hparams
									for mode in ["train", "dev", "test"]:
													__a :						Union[str, Any]			       =  self._feature_file(_UpperCAmelCase     )
													if os.path.exists(_UpperCAmelCase     ) and not args.overwrite_cache:
																	logger.info('''Loading features from cached file %s'''	,			_UpperCAmelCase     )
																	__a :						Any			       =  torch.load(_UpperCAmelCase     )
													else:
																	logger.info('''Creating features from dataset file at %s'''	,			args.data_dir     )
																	__a :						Any			       =  self.token_classification_task.read_examples_from_file(args.data_dir	,			_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  self.token_classification_task.convert_examples_to_features(
																	    _UpperCAmelCase	,			self.labels	,			args.max_seq_length	,			self.tokenizer	,			cls_token_at_end=bool(self.config.model_type in ['''xlnet''']     )	,			cls_token=self.tokenizer.cls_token	,			cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0	,			sep_token=self.tokenizer.sep_token	,			sep_token_extra=_UpperCAmelCase	,			pad_on_left=bool(self.config.model_type in ['''xlnet''']     )	,			pad_token=self.tokenizer.pad_token_id	,			pad_token_segment_id=self.tokenizer.pad_token_type_id	,			pad_token_label_id=self.pad_token_label_id	,			)
																	logger.info('''Saving features into cached file %s'''	,			_UpperCAmelCase     )
																	torch.save(_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = False     ):
									__a :						Any			       =  self._feature_file(_UpperCAmelCase     )
									logger.info('''Loading features from cached file %s'''	,			_UpperCAmelCase     )
									__a :						Dict			       =  torch.load(_UpperCAmelCase     )
									__a :						int			       =  torch.tensor([f.input_ids for f in features]	,			dtype=torch.long     )
									__a :						Optional[int]			       =  torch.tensor([f.attention_mask for f in features]	,			dtype=torch.long     )
									if features[0].token_type_ids is not None:
													__a :						Union[str, Any]			       =  torch.tensor([f.token_type_ids for f in features]	,			dtype=torch.long     )
									else:
													__a :						Optional[Any]			       =  torch.tensor([0 for f in features]	,			dtype=torch.long     )
													# HACK(we will not use this anymore soon)
									__a :						Optional[Any]			       =  torch.tensor([f.label_ids for f in features]	,			dtype=torch.long     )
									return DataLoader(
									    TensorDataset(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )	,			batch_size=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									"""Compute validation""" ""
									__a :						int			       =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
									if self.config.model_type != "distilbert":
													__a :						str			       =  (
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids
									__a :						List[str]			       =  self(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  outputs[:2]
									__a :						Optional[int]			       =  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  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  torch.stack([x['''val_loss'''] for x in outputs]     ).mean()
									__a :						str			       =  np.concatenate([x['''pred'''] for x in outputs]	,			axis=0     )
									__a :						Tuple			       =  np.argmax(_UpperCAmelCase	,			axis=2     )
									__a :						int			       =  np.concatenate([x['''target'''] for x in outputs]	,			axis=0     )

									__a :						Optional[Any]			       =  dict(enumerate(self.labels     )     )
									__a :						int			       =  [[] for _ in range(out_label_ids.shape[0]     )]
									__a :						Optional[int]			       =  [[] for _ in range(out_label_ids.shape[0]     )]

									for i in range(out_label_ids.shape[0]     ):
													for j in range(out_label_ids.shape[1]     ):
																	if out_label_ids[i, j] != self.pad_token_label_id:
																					out_label_list[i].append(label_map[out_label_ids[i][j]]     )
																					preds_list[i].append(label_map[preds[i][j]]     )

									__a :						str			       =  {
									    '''val_loss''': val_loss_mean,
									    '''accuracy_score''': accuracy_score(_UpperCAmelCase	,			_UpperCAmelCase     ),
									    '''precision''': precision_score(_UpperCAmelCase	,			_UpperCAmelCase     ),
									    '''recall''': recall_score(_UpperCAmelCase	,			_UpperCAmelCase     ),
									    '''f1''': fa_score(_UpperCAmelCase	,			_UpperCAmelCase     ),
									}

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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									# when stable
									__a :						Union[str, Any]			       =  self._eval_end(_UpperCAmelCase     )
									__a :						List[Any]			       =  ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									# updating to test_epoch_end instead of deprecated test_end
									__a :						List[Any]			       =  self._eval_end(_UpperCAmelCase     )

									# Converting to the dict required by pl
									# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
									# pytorch_lightning/trainer/logging.py#L139
									__a :						Optional[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  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									# Add NER specific options
									BaseTransformer.add_model_specific_args(_UpperCAmelCase	,			_UpperCAmelCase     )
									parser.add_argument(
									    '''--task_type'''	,			default='''NER'''	,			type=_UpperCAmelCase	,			help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
									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(
									    '''--labels'''	,			default=''''''	,			type=_UpperCAmelCase	,			help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''	,			)
									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


if __name__ == "__main__":
		A 			=   argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		A 			=   NERTransformer.add_model_specific_args(parser, os.getcwd())
		A 			=   parser.parse_args()
		A 			=   NERTransformer(args)
		A 			=   generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				A 			=   sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
				A 			=   model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float   ,       )		->		float:

				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()