





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  (a__ :Tuple							):






							"""simple docstring"""





							UpperCamelCase__					  =      filter(lambda a__							: p.requires_grad ,      model.parameters()							)
							UpperCamelCase__					  =      sum([np.prod(p.size()							) for p in model_parameters]							)
							return params


UpperCamelCase__           =			logging.getLogger(__name__)





def      _UpperCamelCase  (a__ :Tuple ,      a__ :List[str]							):






							"""simple docstring"""





							if metric == "rouge2":
														UpperCamelCase__					  =      "{val_avg_rouge2:.4f}-{step_count}"
							elif metric == "bleu":
														UpperCamelCase__					  =      "{val_avg_bleu:.4f}-{step_count}"
							elif metric == "em":
														UpperCamelCase__					  =      "{val_avg_em:.4f}-{step_count}"
							elif metric == "loss":
														UpperCamelCase__					  =      "{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__					  =      ModelCheckpoint(
							    dirpath=a__ ,      filename=a__ ,      monitor=f"""val_{metric}""" ,      mode="""max""" ,      save_top_k=1 ,      every_n_epochs=1 ,      )

							return checkpoint_callback





def      _UpperCamelCase  (a__ :int ,      a__ :Optional[Any]							):






							"""simple docstring"""





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

class 							__SCREAMING_SNAKE_CASE       (  pl.Callback							):






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

														if not save_generations:
																					return

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






							@rank_zero_only
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														try:
																					UpperCamelCase__					  =      pl_module.model.model.num_parameters()
														except AttributeError:
																					UpperCamelCase__					  =      pl_module.model.num_parameters()

														UpperCamelCase__					  =      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 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														save_json(pl_module.metrics	,			pl_module.metrics_save_path	)
														return self._write_logs(a_	,			a_	,			"""test"""	)







							@rank_zero_only
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														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'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		1
	while index < n_element:
		while hamming_list[i] * 2 <= hamming_list[-1]:
			i += 1
		while hamming_list[j] * 3 <= hamming_list[-1]:
			j += 1
		while hamming_list[k] * 5 <= hamming_list[-1]:
			k += 1
		hamming_list.append(
		    min(hamming_list[i] * 2							,hamming_list[j] * 3							,hamming_list[k] * 5							)							)
		index += 1
	return hamming_list


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
					print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
					lowerCAmelCase   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


