


'''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 import save_json




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




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


__magic_name__   :   Optional[int]  							=logging.getLogger(__name__)






class 		UpperCamelCase_		(					pl.Callback   ):





						"""simple docstring"""




						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Optional[Any]:
										__magic_name__	  = {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 					__A					( self				:					str	,				_lowerCamelCase				:					pl.Trainer	,				_lowerCamelCase				:					pl.LightningModule	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=True  )							->					None:
										logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'  )
										__magic_name__	  = 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
										__magic_name__	  = Path(pl_module.hparams.output_dir  )
										if type_path == "test":
														__magic_name__	  = od / "test_results.txt"
														__magic_name__	  = 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.
														__magic_name__	  = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
														__magic_name__	  = 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
																		__magic_name__	  = metrics[key]
																		if isinstance(_lowerCamelCase	,				torch.Tensor  ):
																						__magic_name__	  = val.item()
																		__magic_name__	  = f'{key}: {val:.6f}\n'
																		writer.write(_lowerCamelCase  )

										if not save_generations:
														return

										if "preds" in metrics:
														__magic_name__	  = "\n".join(metrics["preds"]  )
														generations_file.open("w+"  ).write(_lowerCamelCase  )
						@rank_zero_only
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					Dict:
										try:
														__magic_name__	  = pl_module.model.model.num_parameters()
										except AttributeError:
														__magic_name__	  = pl_module.model.num_parameters()

										__magic_name__	  = 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 					__A					( self				:					Dict	,				_lowerCamelCase				:					pl.Trainer	,				_lowerCamelCase				:					pl.LightningModule  )							->					Tuple:
										save_json(pl_module.metrics	,				pl_module.metrics_save_path  )
										return self._write_logs(_lowerCamelCase	,				_lowerCamelCase	,				"test"  )

						@rank_zero_only
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					pl.Trainer	,				_lowerCamelCase				:					int  )							->					Optional[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      __snake_case       (       lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				Any	,  lowerCamelCase_ :				Tuple=1	,  lowerCamelCase_ :				Dict=False						):
				'''simple docstring'''




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

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




def      __snake_case       (       lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




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







'''simple docstring'''
import os
import sys
import time

from . import transposition_cipher as trans_cipher




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = "Prehistoric Men.txt"
				__magic_name__	  = "Output.txt"
				__magic_name__	  = int(input("Enter key: "						)						)
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				if not os.path.exists(lowerCamelCase_						):
								print(F'File {input_file} does not exist. Quitting...'						)
								sys.exit()
				if os.path.exists(lowerCamelCase_						):
								print(F'Overwrite {output_file}? [y/n]'						)
								__magic_name__	  = input("> "						)
								if not response.lower().startswith("y"						):
												sys.exit()

				__magic_name__	  = time.time()
				if mode.lower().startswith("e"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif mode.lower().startswith("d"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

				with open(lowerCamelCase_	,  "w"						) as output_obj:
								output_obj.write(lowerCamelCase_						)

				__magic_name__	  = round(time.time() - start_time	,  2						)
				print(("Done (", total_time, "seconds )")						)


if __name__ == "__main__":
						main()



