



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 (       lowerCamelCase__						):

    """simple docstring"""


    lowercase__					:   List[str]													=					filter(lambda lowerCamelCase__						: p.requires_grad					,			model.parameters()						)
    lowercase__					:   Tuple													=					sum([np.prod(p.size()						) for p in model_parameters]						)
    return params


lowerCAmelCase__					  =  logging.getLogger(__name__)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    if metric == "rouge2":
        lowercase__					:   List[Any]													=					"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        lowercase__					:   int													=					"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        lowercase__					:   Optional[int]													=					"{val_avg_em:.4f}-{step_count}"
    elif metric == "loss":
        lowercase__					:   Optional[Any]													=					"{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."						)

    lowercase__					:   Tuple													=					ModelCheckpoint(
        dirpath=__lowerCamelCase					,			filename=__lowerCamelCase					,			monitor=F"""val_{metric}"""					,			mode="max"					,			save_top_k=1					,			every_n_epochs=1					,			)

    return checkpoint_callback


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


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

class 				snake_case__(pl.Callback      ):

     """simple docstring"""






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

         if not save_generations:
             return

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







     @rank_zero_only
     def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Union[str, Any]					):
         try:
             lowercase__					:   Tuple													=					pl_module.model.model.num_parameters()
         except AttributeError:
             lowercase__					:   Dict													=					pl_module.model.num_parameters()

         lowercase__					:   Any													=					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 :     Dict				,       SCREAMING_SNAKE_CASE :     pl.Trainer				,       SCREAMING_SNAKE_CASE :     pl.LightningModule					):
         save_json(pl_module.metrics				,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCamelCase				,       _lowerCamelCase				,       "test"					)







     @rank_zero_only
     def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     pl.Trainer				,       SCREAMING_SNAKE_CASE :     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")









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					len(lowerCamelCase__						) if (len(lowerCamelCase__						) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8						)					,			"Stack".center(lowerCamelCase__						)					,			"Postfix".center(lowerCamelCase__						)					,			sep=" | "					,			)
				print("-" * (print_width * 3 + 7)						)

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase__						)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase__						)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase__						) == 0:
																stack.append(lowerCamelCase__						)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
																stack.append(lowerCamelCase__						)  # push x to stack

								print(
								    x.center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				while len(lowerCamelCase__						) > 0:  # while stack is not empty
								post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								print(
								    " ".center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				return "".join(lowerCamelCase__						)  # return Postfix as str


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase__						)						))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
				lowerCAmelCase__					  =  input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
				lowerCAmelCase__					  =  ''''''.join(Infix.split())  # Remove spaces from the input
				print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')




