
"""simple docstring"""






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 import save_json



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


_lowercase							 =							logging.getLogger(__name__)

class 				__a    (   pl.Callback					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Union[str, Any]:




													'''simple docstring'''
													__lowercase						      =					{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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=True					) ->       None:




													'''simple docstring'''
													logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
													__lowercase						      =					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
													__lowercase						      =					Path(pl_module.hparams.output_dir					)
													if type_path == "test":
																			__lowercase						      =					od / "test_results.txt"
																			__lowercase						      =					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.
																			__lowercase						      =					od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
																			__lowercase						      =					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
																									__lowercase						      =					metrics[key]
																									if isinstance(_lowerCamelCase     ,						torch.Tensor					):
																															__lowercase						      =					val.item()
																									__lowercase						      =					f'''{key}: {val:.6f}\n'''
																									writer.write(_lowerCamelCase					)

													if not save_generations:
																			return

													if "preds" in metrics:
																			__lowercase						      =					"\n".join(metrics["preds"]					)
																			generations_file.open("w+"					).write(_lowerCamelCase					)
							@rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       int:




													'''simple docstring'''
													try:
																			__lowercase						      =					pl_module.model.model.num_parameters()
													except AttributeError:
																			__lowercase						      =					pl_module.model.num_parameters()

													__lowercase						      =					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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[Any]:




													'''simple docstring'''
													save_json(pl_module.metrics     ,						pl_module.metrics_save_path					)
													return self._write_logs(_lowerCamelCase     ,						_lowerCamelCase     ,						"test"					)





							@rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




													'''simple docstring'''
													save_json(pl_module.metrics     ,						pl_module.metrics_save_path					)
													# Uncommenting this will save val generations
													# return self._write_logs(trainer, pl_module, "valid")





def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=1 ,      __magic_name__=False			)	->Union[str, Any]:
						if metric == "rouge2":
												__lowercase						      =					"{val_avg_rouge2:.4f}-{step_count}"
						elif metric == "bleu":
												__lowercase						      =					"{val_avg_bleu:.4f}-{step_count}"
						elif metric == "loss":
												__lowercase						      =					"{val_avg_loss:.4f}-{step_count}"
						else:
												raise NotImplementedError(
												    F'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
												    " this function."			)

						__lowercase						      =					ModelCheckpoint(
						    dirpath=__magic_name__ ,      filename=__magic_name__ ,      monitor=F'''val_{metric}''' ,      mode="min" if "loss" in metric else "max" ,      save_top_k=__magic_name__ ,      )
						return checkpoint_callback







def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Any:
						return EarlyStopping(
						    monitor=F'''val_{metric}''' ,      mode="min" if "loss" in metric else "max" ,      patience=__magic_name__ ,      verbose=__magic_name__ ,      )







"""simple docstring"""






from scipy.constants import g



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->float:
			if mass < 0:
						# handling of negative values of mass
						raise ValueError("The mass of a body cannot be negative"			)
			if height < 0:
						# handling of negative values of height
						raise ValueError("The height above the ground cannot be negative"			)
			return mass * g * height


if __name__ == "__main__":
					from doctest import testmod

					testmod(name='''potential_energy''')





