



'''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 				lowerCAmelCase_   (	_lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		filter(lambda _lowerCamelCase					: p.requires_grad       ,      model.parameters()					)
		__SCREAMING_SNAKE_CASE       :		Any           =		sum([np.prod(p.size()					) for p in model_parameters]					)
		return params


UpperCamelCase__       :	Tuple    = logging.getLogger(__name__)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any       ,      _lowerCamelCase:   str					):
		if metric == "rouge2":
				__SCREAMING_SNAKE_CASE       :		str           =		"""{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "em":
				__SCREAMING_SNAKE_CASE       :		int           =		"""{val_avg_em:.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."""					)

		__SCREAMING_SNAKE_CASE       :		Any           =		ModelCheckpoint(
		    dirpath=_lowerCamelCase       ,      filename=_lowerCamelCase       ,      monitor=F"val_{metric}"       ,      mode="""max"""       ,      save_top_k=3       ,      every_n_epochs=1       ,      )
		return checkpoint_callback


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   Tuple					):
		return EarlyStopping(
		    monitor=F"val_{metric}"       ,      mode="""min""" if """loss""" in metric else """max"""       ,      patience=_lowerCamelCase       ,      verbose=_lowerCamelCase       ,      )






class 						_UpperCamelCase  (							pl.Callback    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		{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 UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : pl.Trainer							,     lowerCAmelCase__     : pl.LightningModule							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Dict=True						):





					"""simple docstring"""






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

					if not save_generations:
							return

					if "preds" in metrics:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""\n""".join(metrics["""preds"""]						)
							generations_file.open("""w+"""						).write(lowerCAmelCase__						)

			@rank_zero_only
			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					try:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pl_module.model.model.num_parameters()
					except AttributeError:
							__SCREAMING_SNAKE_CASE       :		int           =		pl_module.model.num_parameters()

					__SCREAMING_SNAKE_CASE       :		Tuple           =		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 UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : pl.Trainer							,     lowerCAmelCase__     : pl.LightningModule						):





					"""simple docstring"""






					save_json(pl_module.metrics							,     pl_module.metrics_save_path						)
					return self._write_logs(lowerCAmelCase__							,     lowerCAmelCase__							,     """test"""						)



			@rank_zero_only
			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : pl.Trainer							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					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 string


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		""""""
		for i in sequence:
				__SCREAMING_SNAKE_CASE       :		Any           =		ord(_lowerCamelCase					)
				if 65 <= extract <= 90:
						output += chr(1_55 - extract					)
				elif 97 <= extract <= 1_22:
						output += chr(2_19 - extract					)
				else:
						output += i
		return output


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		string.ascii_letters
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(_lowerCamelCase					)] if c in letters else c for c in sequence					)


def 				lowerCAmelCase_   (	):
		from timeit import timeit

		print("""Running performance benchmarks..."""					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""from string import printable ; from __main__ import atbash, atbash_slow"""
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)
		print(F">      atbash(): {timeit('atbash(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)


if __name__ == "__main__":
						for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
												print(f"{example} encrypted in atbash: {atbash(example)}")
						benchmark()