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  snake_case						(__lowercase						)	->	Optional[Any]:

						'''simple docstring'''



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


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.getLogger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



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

						_snake_case						:					List[str]    =   ModelCheckpoint(
						    dirpath=__lowercase ,      filename=__lowercase ,      monitor=F"""val_{metric}""" ,      mode="max" ,      save_top_k=1 ,      every_n_epochs=1 ,      )

						return checkpoint_callback
def  snake_case						(__lowercase ,      __lowercase						)	->	Any:

						'''simple docstring'''



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



class        lowercase_   (					pl.Callback		):



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

													if not save_generations:
																			return

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



							@rank_zero_only
							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													try:
																			_snake_case						:					Any    =   pl_module.model.model.num_parameters()
													except AttributeError:
																			_snake_case						:					Optional[Any]    =   pl_module.model.num_parameters()

													_snake_case						:					Optional[Any]    =   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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													save_json(pl_module.metrics				,    pl_module.metrics_save_path    )
													return self._write_logs(lowercase_				,    lowercase_				,    "test"    )



							@rank_zero_only
							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													save_json(pl_module.metrics				,    pl_module.metrics_save_path    )
													# Uncommenting this will save val generations
													# return self._write_logs(trainer, pl_module, "valid")
from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__SCREAMING_SNAKE_CASE						:   str         =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE						:   list[int]         =			[ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE						:   set[int]         =			{ord(char) for char in VALID_CHARS}

__SCREAMING_SNAKE_CASE						:   list[str]         =			["the", "be", "to", "of", "and", "in", "that", "have"]
def  snake_case						(__lowercase ,      __lowercase						)	->	str | None:

						'''simple docstring'''



						_snake_case						:					str    =   ""
						_snake_case						:					int
						_snake_case						:					int
						_snake_case						:					int

						for keychar, cipherchar in zip(cycle(__lowercase						) ,      __lowercase						):
												_snake_case						:					str    =   cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowercase						)

						return decoded
def  snake_case						(__lowercase						)	->	list[str]:

						'''simple docstring'''



						_snake_case						:					list[str]    =   []
						for key in product(__lowercase ,      repeat=3						):
												_snake_case						:					Union[str, Any]    =   try_key(__lowercase ,      __lowercase						)
												if encoded is not None:
																		possibles.append(__lowercase						)
						return possibles
def  snake_case						(__lowercase ,      __lowercase						)	->	list[str]:

						'''simple docstring'''



						return [possible for possible in possibles if common_word in possible.lower()]
def  snake_case						(__lowercase = "p059_cipher.txt"						)	->	int:

						'''simple docstring'''



						_snake_case						:					list[int]
						_snake_case						:					list[str]
						_snake_case						:					str
						_snake_case						:					str
						_snake_case						:					str    =   Path(__lowercase						).parent.joinpath(__lowercase						).read_text(encoding="utf-8"						)

						_snake_case						:					Dict    =   [int(__lowercase						) for number in data.strip().split(","						)]

						_snake_case						:					Tuple    =   filter_valid_chars(__lowercase						)
						for common_word in COMMON_WORDS:
												_snake_case						:					Optional[int]    =   filter_common_word(__lowercase ,      __lowercase						)
												if len(__lowercase						) == 1:
																		break

						_snake_case						:					int    =   possibles[0]
						return sum(ord(__lowercase						) for char in decoded_text						)


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