


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


__A	=logging.getLogger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			if metric == "rouge2":
						UpperCAmelCase__  :					List[str]        =     """{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						UpperCAmelCase__  :					int        =     """{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						UpperCAmelCase__  :					str        =     """{val_avg_em:.4f}-{step_count}"""
			elif metric == "loss":
						UpperCAmelCase__  :					str        =     """{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__  :					Optional[Any]        =     ModelCheckpoint(
			    dirpath=snake_case__ ,      filename=snake_case__ ,      monitor=f'''val_{metric}''' ,      mode="""max""" ,      save_top_k=1 ,      every_n_epochs=1 ,      )

			return checkpoint_callback







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			return EarlyStopping(
			    monitor=f'''val_{metric}''' ,      mode="""min""" if """loss""" in metric else """max""" ,      patience=snake_case__ ,      verbose=snake_case__ ,      )


class 		_snake_case					(     pl.Callback	):




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     {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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=True):
								logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''')
								UpperCAmelCase__  :					Union[str, Any]        =     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__  :					str        =     Path(pl_module.hparams.output_dir)
								if type_path == "test":
											UpperCAmelCase__  :					List[Any]        =     od / """test_results.txt"""
											UpperCAmelCase__  :					Optional[int]        =     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__  :					List[str]        =     od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
											UpperCAmelCase__  :					Dict        =     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__  :					Tuple        =     val.item()
														UpperCAmelCase__  :					List[str]        =     f'''{key}: {val:.6f}\n'''
														writer.write(lowerCAmelCase_)

								if not save_generations:
											return

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




					@rank_zero_only
					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								try:
											UpperCAmelCase__  :					List[str]        =     pl_module.model.model.num_parameters()
								except AttributeError:
											UpperCAmelCase__  :					Optional[int]        =     pl_module.model.num_parameters()

								UpperCAmelCase__  :					Tuple        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								save_json(pl_module.metrics							,							pl_module.metrics_save_path)
								return self._write_logs(lowerCAmelCase_							,							lowerCAmelCase_							,							"""test""")




					@rank_zero_only
					def 				snake_case__						(      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'''



from itertools import permutations







def     _UpperCamelCase      (		UpperCamelCase__					):
			if num[3] % 2 != 0:
						return False

			if (num[2] + num[3] + num[4]) % 3 != 0:
						return False

			if num[5] % 5 != 0:
						return False

			UpperCAmelCase__  :					Optional[int]        =     [7, 1_1, 1_3, 1_7]
			for i, test in enumerate(UpperCamelCase__					):
						if (num[i + 4] * 1_0_0 + num[i + 5] * 1_0 + num[i + 6]) % test != 0:
									return False
			return True







def     _UpperCamelCase      (		UpperCamelCase__ = 1_0					):
			return sum(
			    int("""""".join(map(UpperCamelCase__ ,      UpperCamelCase__					)					)					)
			    for num in permutations(range(UpperCamelCase__					)					)
			    if is_substring_divisible(UpperCamelCase__					)					)


if __name__ == "__main__":
		print(f"""{solution() = }""")