






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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int   )						->     Optional[Any]:






	"""simple docstring"""



	UpperCamelCase	:Any		=      filter(lambda __magic_name__   : p.requires_grad      ,     model.parameters()   )
	UpperCamelCase	:Dict		=      sum([np.prod(p.size()   ) for p in model_parameters]   )
	return params


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

class        _SCREAMING_SNAKE_CASE			(     pl.Callback ):





	def 		_A					(						self							:   int					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   str      ):
		UpperCamelCase	: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 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   pl.Trainer					,    __lowerCamelCase							:   pl.LightningModule					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Any=True      ):
		logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
		UpperCamelCase	: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
		UpperCamelCase	:Any		=      Path(pl_module.hparams.output_dir      )
		if type_path == "test":
			UpperCamelCase	:Optional[Any]		=      od / """test_results.txt"""
			UpperCamelCase	: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.
			UpperCamelCase	:Dict		=      od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
			UpperCamelCase	:Union[str, 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
				UpperCamelCase	:int		=      metrics[key]
				if isinstance(__lowerCamelCase					,    torch.Tensor      ):
					UpperCamelCase	:Optional[int]		=      val.item()
				UpperCamelCase	:int		=      F"""{key}: {val:.6f}\n"""
				writer.write(__lowerCamelCase      )

		if not save_generations:
			return

		if "preds" in metrics:
			UpperCamelCase	:Union[str, Any]		=      """\n""".join(metrics["""preds"""]      )
			generations_file.open("""w+"""      ).write(__lowerCamelCase      )





	@rank_zero_only
	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   List[Any]      ):
		try:
			UpperCamelCase	:List[str]		=      pl_module.model.model.num_parameters()
		except AttributeError:
			UpperCamelCase	:List[Any]		=      pl_module.model.num_parameters()

		UpperCamelCase	:List[Any]		=      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 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   pl.Trainer					,    __lowerCamelCase							:   pl.LightningModule      ):
		save_json(pl_module.metrics					,    pl_module.metrics_save_path      )
		return self._write_logs(__lowerCamelCase					,    __lowerCamelCase					,    """test"""      )





	@rank_zero_only
	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   pl.Trainer					,    __lowerCamelCase							:   Dict      ):
		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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]      ,     __magic_name__   :    str      ,     __magic_name__   :    int=1      ,     __magic_name__   :    str=False   )						->     Tuple:






	"""simple docstring"""



	if metric == "rouge2":
		UpperCamelCase	:Optional[int]		=      """{val_avg_rouge2:.4f}-{step_count}"""
	elif metric == "bleu":
		UpperCamelCase	:Tuple		=      """{val_avg_bleu:.4f}-{step_count}"""
	elif metric == "loss":
		UpperCamelCase	:int		=      """{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."""   )

	UpperCamelCase	:int		=      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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    List[str]   )						->     Optional[Any]:






	"""simple docstring"""



	return EarlyStopping(
	    monitor=f"""val_{metric}"""      ,     mode="""min""" if """loss""" in metric else """max"""      ,     patience=__magic_name__      ,     verbose=__magic_name__      ,     )








from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_					:		Union[str, Any]           = 6.6_743E-11  # unit of G : m^3 * kg^-1 * s^-2

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float   )						->     dict[str, float]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      mass_a * mass_a

	if (force, mass_a, mass_a, distance).count(0   ) != 1:
		raise ValueError("""One and only one argument must be 0"""   )
	if force < 0:
		raise ValueError("""Gravitational force can not be negative"""   )
	if distance < 0:
		raise ValueError("""Distance can not be negative"""   )
	if mass_a < 0 or mass_a < 0:
		raise ValueError("""Mass can not be negative"""   )
	if force == 0:
		UpperCamelCase	:Optional[int]		=      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		UpperCamelCase	:Any		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		UpperCamelCase	:Optional[int]		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		UpperCamelCase	:str		=      (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
		return {"distance": distance}
	raise ValueError("""One and only one argument must be 0"""   )


# Run doctest
if __name__ == "__main__":
	import doctest

	doctest.testmod()
