
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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						filter(lambda lowerCAmelCase__		: p.requires_grad			,      model.parameters()		)
					lowercase						     =						sum([np.prod(p.size()		) for p in model_parameters]		)
					return params


lowercase__		:Dict			  =   logging.getLogger(__name__)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if metric == "rouge2":
										lowercase						     =						'''{val_avg_rouge2:.4f}-{step_count}'''
					elif metric == "bleu":
										lowercase						     =						'''{val_avg_bleu:.4f}-{step_count}'''
					elif metric == "em":
										lowercase						     =						'''{val_avg_em:.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.'''		)

					lowercase						     =						ModelCheckpoint(
					    dirpath=lowerCAmelCase__			,      filename=lowerCAmelCase__			,      monitor=f'val_{metric}'			,      mode='''max'''			,      save_top_k=3			,      every_n_epochs=1			,      )
					return checkpoint_callback



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					return EarlyStopping(
					    monitor=f'val_{metric}'			,      mode='''min''' if '''loss''' in metric else '''max'''			,      patience=lowerCAmelCase__			,      verbose=lowerCAmelCase__			,      )







class     lowercase		( pl.Callback		):




							def 			A__				(    self       ,A__       ,A__):
												lowercase						     =						{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 			A__				(    self       ,A__       ,A__       ,A__       ,A__=True):
												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=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
																						lowercase						     =						metrics[key]
																						if isinstance(A__       ,torch.Tensor):
																											lowercase						     =						val.item()
																						lowercase						     =						f'{key}: {val:.6f}\n'
																						writer.write(A__)

												if not save_generations:
																	return

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




							@rank_zero_only
							def 			A__				(    self       ,A__       ,A__):
												try:
																	lowercase						     =						pl_module.model.model.num_parameters()
												except AttributeError:
																	lowercase						     =						pl_module.model.num_parameters()

												lowercase						     =						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 			A__				(    self       ,A__       ,A__):
												save_json(pl_module.metrics       ,pl_module.metrics_save_path)
												return self._write_logs(A__       ,A__       ,'''test''')





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






lowercase__		:Any			  =   8.3_144_598



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if temperature < 0:
										raise Exception('''Temperature cannot be less than 0 K'''		)
					if molar_mass <= 0:
										raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol'''		)
					else:
										return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							lowercase__		:Optional[Any]			  =   300
							lowercase__		:List[Any]			  =   28
							lowercase__		:Dict			  =   rms_speed_of_molecule(temperature, molar_mass)
							print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')




