

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def       a__					(		UpperCAmelCase							:      str      )  ->						Union[str, Any]:
  UpperCAmelCase	:							Optional[int]						=			filter(lambda UpperCAmelCase      : p.requires_grad    ,			model.parameters()      )
  UpperCAmelCase	:							Union[str, Any]						=			sum([np.prod(p.size()      ) for p in model_parameters]      )
  return params


_lowerCamelCase    :       int        =							logging.getLogger(__name__)

def       a__					(		UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      Any      )  ->						int:
  if metric == "rouge2":
    UpperCAmelCase	:							List[Any]						=			'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    UpperCAmelCase	:							List[Any]						=			'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    UpperCAmelCase	:							Tuple						=			'''{val_avg_em:.4f}-{step_count}'''
  elif metric == "loss":
    UpperCAmelCase	:							Tuple						=			'''{val_avg_loss:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
        ''' function.'''      )

  UpperCAmelCase	:							Optional[int]						=			ModelCheckpoint(
      dirpath=UpperCAmelCase    ,			filename=UpperCAmelCase    ,			monitor=f'''val_{metric}'''    ,			mode='''max'''    ,			save_top_k=1    ,			every_n_epochs=1    ,			)

  return checkpoint_callback

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      List[str]      )  ->						Union[str, Any]:
  return EarlyStopping(
      monitor=f'''val_{metric}'''    ,			mode='''min''' if '''loss''' in metric else '''max'''    ,			patience=UpperCAmelCase    ,			verbose=UpperCAmelCase    ,			)






class  __UpperCAmelCase					(	pl.Callback						):



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Union[str, Any],		__A							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			{F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
     pl_module.logger.log_metrics(__A						)



   @rank_zero_only
   def 	__magic_name__   (							self							:					Any,		__A							:					pl.Trainer,		__A							:					pl.LightningModule,		__A							:					str,		__A							:					List[Any]=True						):
     logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''						)
     UpperCAmelCase	:							Optional[Any]						=			trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}						)
     # Log results
     UpperCAmelCase	:							Dict						=			Path(pl_module.hparams.output_dir						)
     if type_path == "test":
       UpperCAmelCase	:							int						=			od / '''test_results.txt'''
       UpperCAmelCase	:							int						=			od / '''test_generations.txt'''
     else:
       # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
       # If people want this it will be easy enough to add back.
       UpperCAmelCase	:							Dict						=			od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
       UpperCAmelCase	:							int						=			od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
       results_file.parent.mkdir(exist_ok=__A						)
       generations_file.parent.mkdir(exist_ok=__A						)
     with open(__A,		'''a+'''						) as writer:
       for key in sorted(__A						):
         if key in ["log", "progress_bar", "preds"]:
           continue
         UpperCAmelCase	:							List[str]						=			metrics[key]
         if isinstance(__A,		torch.Tensor						):
           UpperCAmelCase	:							str						=			val.item()
         UpperCAmelCase	:							List[Any]						=			F'''{key}: {val:.6f}\n'''
         writer.write(__A						)

     if not save_generations:
       return

     if "preds" in metrics:
       UpperCAmelCase	:							Dict						=			'''\n'''.join(metrics['''preds''']						)
       generations_file.open('''w+'''						).write(__A						)



   @rank_zero_only
   def 	__magic_name__   (							self							:					int,		__A							:					Optional[int],		__A							:					List[Any]						):
     try:
       UpperCAmelCase	:							Union[str, Any]						=			pl_module.model.model.num_parameters()
     except AttributeError:
       UpperCAmelCase	:							Optional[Any]						=			pl_module.model.num_parameters()

     UpperCAmelCase	:							List[Any]						=			count_trainable_parameters(__A						)
     # mp stands for million parameters
     trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6}						)



   @rank_zero_only
   def 	__magic_name__   (							self							:					List[Any],		__A							:					pl.Trainer,		__A							:					pl.LightningModule						):
     save_json(pl_module.metrics,		pl_module.metrics_save_path						)
     return self._write_logs(__A,		__A,		'''test'''						)





   @rank_zero_only
   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					pl.Trainer,		__A							:					Any						):
     save_json(pl_module.metrics,		pl_module.metrics_save_path						)
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")








from math import log

from scipy.constants import Boltzmann, physical_constants

_lowerCamelCase    :       Tuple        =							3_0_0  # TEMPERATURE (unit = K)

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			UpperCAmelCase							:      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()





