




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


UpperCAmelCase_    =logging.getLogger(__name__)


class 				__UpperCamelCase      (			pl.Callback	):





				'''simple docstring'''



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		{F"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
											pl_module.logger.log_metrics(_UpperCamelCase    )



				@rank_zero_only
				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_=True    ):
											logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""    )
											lowerCAmelCase =		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
											lowerCAmelCase =		Path(pl_module.hparams.output_dir    )
											if type_path == "test":
																		lowerCAmelCase =		od / '''test_results.txt'''
																		lowerCAmelCase =		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.
																		lowerCAmelCase =		od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
																		lowerCAmelCase =		od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																		results_file.parent.mkdir(exist_ok=_UpperCamelCase    )
																		generations_file.parent.mkdir(exist_ok=_UpperCamelCase    )
											with open(_UpperCamelCase							,			'''a+'''    ) as writer:
																		for key in sorted(_UpperCamelCase    ):
																									if key in ["log", "progress_bar", "preds"]:
																																continue
																									lowerCAmelCase =		metrics[key]
																									if isinstance(_UpperCamelCase							,			torch.Tensor    ):
																																lowerCAmelCase =		val.item()
																									lowerCAmelCase =		F"""{key}: {val:.6f}\n"""
																									writer.write(_UpperCamelCase    )

											if not save_generations:
																		return

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



				@rank_zero_only
				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											try:
																		lowerCAmelCase =		pl_module.model.model.num_parameters()
											except AttributeError:
																		lowerCAmelCase =		pl_module.model.num_parameters()

											lowerCAmelCase =		count_trainable_parameters(_UpperCamelCase    )
											# 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							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											save_json(pl_module.metrics							,			pl_module.metrics_save_path    )
											return self._write_logs(_UpperCamelCase							,			_UpperCamelCase							,			'''test'''    )






				@rank_zero_only
				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case=1    ,			_snake_case=False				):






							if metric == "rouge2":
														lowerCAmelCase =		'''{val_avg_rouge2:.4f}-{step_count}'''
							elif metric == "bleu":
														lowerCAmelCase =		'''{val_avg_bleu:.4f}-{step_count}'''
							elif metric == "loss":
														lowerCAmelCase =		'''{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.'''				)

							lowerCAmelCase =		ModelCheckpoint(
							    dirpath=_snake_case    ,			filename=_snake_case    ,			monitor=F"""val_{metric}"""    ,			mode='''min''' if '''loss''' in metric else '''max'''    ,			save_top_k=_snake_case    ,			)
							return checkpoint_callback



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							return EarlyStopping(
							    monitor=F"""val_{metric}"""    ,			mode='''min''' if '''loss''' in metric else '''max'''    ,			patience=_snake_case    ,			verbose=_snake_case    ,			)










import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





