





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      lowerCamelCase__      (__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				filter(lambda __lowerCamelCase						: p.requires_grad,							model.parameters()						)
     _SCREAMING_SNAKE_CASE							:						Dict         =				sum([np.prod(p.size()						) for p in model_parameters]						)
     return params


UpperCamelCase__  			=logging.getLogger(__name__)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     if metric == "rouge2":
          _SCREAMING_SNAKE_CASE							:						int         =				"{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          _SCREAMING_SNAKE_CASE							:						Any         =				"{val_avg_bleu:.4f}-{step_count}"
     elif metric == "em":
          _SCREAMING_SNAKE_CASE							:						Any         =				"{val_avg_em:.4f}-{step_count}"
     elif metric == "loss":
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				"{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."						)

     _SCREAMING_SNAKE_CASE							:						List[Any]         =				ModelCheckpoint(
         dirpath=A__,							filename=A__,							monitor=f"""val_{metric}""",							mode="max",							save_top_k=1,							every_n_epochs=1,							)

     return checkpoint_callback






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     return EarlyStopping(
         monitor=f"""val_{metric}""",							mode="min" if "loss" in metric else "max",							patience=A__,							verbose=A__,							)





class       lowerCAmelCase__(			pl.Callback     ):






       '''simple docstring'''


       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Any:
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
            pl_module.logger.log_metrics(__lowerCamelCase				)




       @rank_zero_only
       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase=True				)      ->  List[str]:
            logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""				)
            _SCREAMING_SNAKE_CASE							:						str         =				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
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				Path(pl_module.hparams.output_dir				)
            if type_path == "test":
                 _SCREAMING_SNAKE_CASE							:						str         =				od / "test_results.txt"
                 _SCREAMING_SNAKE_CASE							:						str         =				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.
                 _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                 _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                 results_file.parent.mkdir(exist_ok=__lowerCamelCase				)
                 generations_file.parent.mkdir(exist_ok=__lowerCamelCase				)
            with open(__lowerCamelCase     ,    "a+"				) as writer:
                 for key in sorted(__lowerCamelCase				):
                      if key in ["log", "progress_bar", "preds"]:
                           continue
                      _SCREAMING_SNAKE_CASE							:						List[Any]         =				metrics[key]
                      if isinstance(__lowerCamelCase     ,    torch.Tensor				):
                           _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				val.item()
                      _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				F"""{key}: {val:.6f}\n"""
                      writer.write(__lowerCamelCase				)

            if not save_generations:
                 return

            if "preds" in metrics:
                 _SCREAMING_SNAKE_CASE							:						List[str]         =				"\n".join(metrics["preds"]				)
                 generations_file.open("w+"				).write(__lowerCamelCase				)




       @rank_zero_only
       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  List[Any]:
            try:
                 _SCREAMING_SNAKE_CASE							:						Dict         =				pl_module.model.model.num_parameters()
            except AttributeError:
                 _SCREAMING_SNAKE_CASE							:						str         =				pl_module.model.num_parameters()

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				count_trainable_parameters(__lowerCamelCase				)
            # 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      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Optional[int]:
            save_json(pl_module.metrics     ,    pl_module.metrics_save_path				)
            return self._write_logs(__lowerCamelCase     ,    __lowerCamelCase     ,    "test"				)







       @rank_zero_only
       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Tuple:
            save_json(pl_module.metrics     ,    pl_module.metrics_save_path				)
            # Uncommenting this will save val generations
            # return self._write_logs(trainer, pl_module, "valid")

'''simple docstring'''
from math import log

from scipy.constants import Boltzmann, physical_constants

lowercase__											=3_00  # TEMPERATURE (unit = K)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      ):

	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()
