




'''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 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

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


a    :							Tuple										=    logging.getLogger(__name__)


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

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

					snake_case_		     =							ModelCheckpoint(
					    dirpath=__UpperCAmelCase, filename=__UpperCAmelCase, monitor=F"val_{metric}", mode='''max''', save_top_k=1, every_n_epochs=1, )

					return checkpoint_callback


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   str:

					'''simple docstring'''

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






class 		a							(  pl.Callback	):


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   List[str]   ):
									snake_case_		     =							{F"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
									pl_module.logger.log_metrics(lowercase_   )


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

									if not save_generations:
														return

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


				@rank_zero_only
				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Optional[int]			,							lowercase_       :   Tuple   ):
									try:
														snake_case_		     =							pl_module.model.model.num_parameters()
									except AttributeError:
														snake_case_		     =							pl_module.model.num_parameters()

									snake_case_		     =							count_trainable_parameters(lowercase_   )
									# 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       :   Dict			,							lowercase_       :   pl.Trainer			,							lowercase_       :   pl.LightningModule   ):
									save_json(pl_module.metrics			,							pl_module.metrics_save_path   )
									return self._write_logs(lowercase_			,							lowercase_			,							'''test'''   )


				@rank_zero_only
				def     A_					(						self       :   Any			,							lowercase_       :   pl.Trainer			,							lowercase_       :   Any   ):
									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'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

