"""simple docstring"""







from __future__ import annotations

import re


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	def alphanum_key(__UpperCAmelCase      ):
		return [int(__UpperCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'  ,__UpperCAmelCase      )]

	return sorted(__UpperCAmelCase  ,key=__UpperCAmelCase      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




"""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   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





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


SCREAMING_SNAKE_CASE						=    logging.getLogger(__name__)



class 			_lowerCamelCase      (pl.Callback  ):







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				{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_     :							Optional[Any]=True    ):

					"""simple docstring"""






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

					if not save_generations:
						return

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







				@rank_zero_only
				def 				__UpperCAmelCase       (			self     :							int      ,       lowerCamelCase_     :							str      ,       lowerCamelCase_     :							Optional[Any]    ):

					"""simple docstring"""






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

					_lowercase			:     str      					=				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     :							List[Any]      ,       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     :							int      ,       lowerCamelCase_     :							pl.Trainer      ,       lowerCamelCase_     :							List[str]    ):

					"""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")



def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=1  ,__UpperCAmelCase=False      ):






	"""simple docstring"""





	if metric == "rouge2":
		_lowercase			:     Tuple      					=				'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		_lowercase			:     Optional[int]      					=				'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "loss":
		_lowercase			:     Union[str, Any]      					=				'{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.'      )

	_lowercase			:     str      					=				ModelCheckpoint(
	    dirpath=__UpperCAmelCase  ,filename=__UpperCAmelCase  ,monitor=F'''val_{metric}'''  ,mode='min' if 'loss' in metric else 'max'  ,save_top_k=__UpperCAmelCase  ,)
	return checkpoint_callback







def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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



