
"""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 (      UpperCamelCase    )       ->    Any:


		"""simple docstring"""





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


__lowerCAmelCase							:  List[str] 				=		logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE       (      pl.Callback					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :int       ,    __magic_name__ :Tuple			)			->   int:

			'''simple docstring'''






			a__   		=     {F"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
			pl_module.logger.log_metrics(__magic_name__			)


	@rank_zero_only
	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :pl.Trainer       ,    __magic_name__ :pl.LightningModule       ,    __magic_name__ :str       ,    __magic_name__ :List[Any]=True			)			->   None:

			'''simple docstring'''






			logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"			)
			a__   		=     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
			a__   		=     Path(pl_module.hparams.output_dir			)
			if type_path == "test":
					a__   		=     od / '''test_results.txt'''
					a__   		=     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.
					a__   		=     od / F"{type_path}_results/{trainer.global_step:05d}.txt"
					a__   		=     od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
					results_file.parent.mkdir(exist_ok=__magic_name__			)
					generations_file.parent.mkdir(exist_ok=__magic_name__			)
			with open(__magic_name__       ,    '''a+'''			) as writer:
					for key in sorted(__magic_name__			):
							if key in ["log", "progress_bar", "preds"]:
									continue
							a__   		=     metrics[key]
							if isinstance(__magic_name__       ,    torch.Tensor			):
									a__   		=     val.item()
							a__   		=     F"{key}: {val:.6f}\n"
							writer.write(__magic_name__			)

			if not save_generations:
					return

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


	@rank_zero_only
	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :int       ,    __magic_name__ :Any			)			->   Union[str, Any]:

			'''simple docstring'''






			try:
					a__   		=     pl_module.model.model.num_parameters()
			except AttributeError:
					a__   		=     pl_module.model.num_parameters()

			a__   		=     count_trainable_parameters(__magic_name__			)
			# 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 :Tuple       ,    __magic_name__ :pl.Trainer       ,    __magic_name__ :pl.LightningModule			)			->   str:

			'''simple docstring'''






			save_json(pl_module.metrics       ,    pl_module.metrics_save_path			)
			return self._write_logs(__magic_name__       ,    __magic_name__       ,    '''test'''			)






	@rank_zero_only
	def    _UpperCamelCase							(  self :Union[str, Any]       ,    __magic_name__ :pl.Trainer       ,    __magic_name__ :List[str]			)			->   Optional[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")

def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase=1						,					UpperCamelCase=False    )       ->    int:


		"""simple docstring"""





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

		a__   		=     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 __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    Tuple:


		"""simple docstring"""





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






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




