





'''simple docstring'''







from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __lt__(  self     :					int					,				__a     :					List[Any]					):
								return self[-1] < other[-1]




				def __eq__(  self     :					List[Any]					,				__a     :					Optional[int]					):
								return self[-1] == other[-1]






def  _lowerCamelCase				( lowercase						:      list						)	->	list:
				_a								= []
				# sort into stacks
				for element in collection:
								_a								= Stack([element]						)
								_a								= bisect_left(lowercase    ,							lowercase						)
								if i != len(lowercase						):
												stacks[i].append(lowercase						)
								else:
												stacks.append(lowercase						)

    # use a heap-based merge to merge stack efficiently
				_a								= merge(*(reversed(lowercase						) for stack in stacks)						)
				return collection


if __name__ == "__main__":
					lowerCAmelCase_					:    List[str]      	=			input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_					:    Optional[int]      	=			[int(item) for item in user_input.split(',')]
					print(patience_sort(unsorted))







'''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_rag import save_json






def  _lowerCamelCase				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				if metric == "rouge2":
								_a								= "{val_avg_rouge2:.4f}-{step_count}"
				elif metric == "bleu":
								_a								= "{val_avg_bleu:.4f}-{step_count}"
				elif metric == "em":
								_a								= "{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."						)

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






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







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

								if not save_generations:
												return

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




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								try:
												_a								= pl_module.model.model.num_parameters()
								except AttributeError:
												_a								= pl_module.model.num_parameters()

								_a								= 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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					int					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
