
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      __UpperCAmelCase    (  snake_case_							:		List[Any]				):



	'''simple docstring'''



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


snake_case_      :					List[Any]				      =     logging.getLogger(__name__)





class 		__lowerCamelCase     (   pl.Callback		):



							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



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



							@rank_zero_only
							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case=True							)						->     None:






								"""simple docstring"""



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

								if not save_generations:
									return

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



							@rank_zero_only
							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



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

								UpperCAmelCase:					Optional[Any] 						=     count_trainable_parameters(__snake_case							)
								# 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	,				__snake_case	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



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




							@rank_zero_only
							def     A__ (     self	,				__snake_case	,				__snake_case							)						->     Union[str, 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      __UpperCAmelCase    (  snake_case_							:		int				,					snake_case_							:		Optional[Any]				,					snake_case_							:		int=1				,					snake_case_							:		int=False				):



	'''simple docstring'''



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

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



def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		List[Any]				):



	'''simple docstring'''



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


def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     str(snake_case_				).strip()
	if not bin_string:
		raise ValueError("Empty string was passed to the function"				)
	UpperCAmelCase:					List[Any] 						=     bin_string[0] == "-"
	if is_negative:
		UpperCAmelCase:					Optional[int] 						=     bin_string[1:]
	if not all(char in "01" for char in bin_string				):
		raise ValueError("Non-binary value was passed to the function"				)
	UpperCAmelCase:					List[str] 						=     0
	for char in bin_string:
		UpperCAmelCase:					Union[str, Any] 						=     2 * decimal_number + int(snake_case_				)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
