



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	:    Tuple	)  ->						Optional[Any]:
					__lowerCAmelCase	   =       filter(lambda __lowerCamelCase	: p.requires_grad				,   model.parameters()	)
					__lowerCAmelCase	   =       sum([np.prod(p.size()	) for p in model_parameters]	)
					return params


lowercase_	       =			logging.getLogger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    List[Any]	)  ->						str:
					if metric == "rouge2":
										__lowerCAmelCase	   =       """{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										__lowerCAmelCase	   =       """{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "em":
										__lowerCAmelCase	   =       """{val_avg_em:.4f}-{step_count}"""
					elif metric == "loss":
										__lowerCAmelCase	   =       """{val_avg_loss:.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."""	)

					__lowerCAmelCase	   =       ModelCheckpoint(
					    dirpath=__lowerCamelCase				,   filename=__lowerCamelCase				,   monitor=f"""val_{metric}"""				,   mode="""max"""				,   save_top_k=1				,   every_n_epochs=1				,   )

					return checkpoint_callback






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    List[Any]	)  ->						Optional[int]:
					return EarlyStopping(
					    monitor=f"""val_{metric}"""				,   mode="""min""" if """loss""" in metric else """max"""				,   patience=__lowerCamelCase				,   verbose=__lowerCamelCase				,   )






class 						__a (     pl.Callback				):



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    Any				,	snake_case_							:    Union[str, Any])->							List[Any]:
						__lowerCAmelCase	   =       {F"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
						pl_module.logger.log_metrics(_a)



	@rank_zero_only
	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    pl.Trainer				,	snake_case_							:    pl.LightningModule				,	snake_case_							:    str				,	snake_case_							:    str=True)->							Optional[int]:
						logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""")
						__lowerCAmelCase	   =       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
						__lowerCAmelCase	   =       Path(pl_module.hparams.output_dir)
						if type_path == "test":
											__lowerCAmelCase	   =       od / """test_results.txt"""
											__lowerCAmelCase	   =       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.
											__lowerCAmelCase	   =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
											__lowerCAmelCase	   =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
											results_file.parent.mkdir(exist_ok=_a)
											generations_file.parent.mkdir(exist_ok=_a)
						with open(_a				,	"""a+""") as writer:
											for key in sorted(_a):
																if key in ["log", "progress_bar", "preds"]:
																					continue
																__lowerCAmelCase	   =       metrics[key]
																if isinstance(_a				,	torch.Tensor):
																					__lowerCAmelCase	   =       val.item()
																__lowerCAmelCase	   =       F"""{key}: {val:.6f}\n"""
																writer.write(_a)

						if not save_generations:
											return

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



	@rank_zero_only
	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    int				,	snake_case_							:    Optional[int])->							List[str]:




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

						__lowerCAmelCase	   =       count_trainable_parameters(_a)
						# 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				,	snake_case_							:    pl.Trainer				,	snake_case_							:    pl.LightningModule)->							Union[str, Any]:
						save_json(pl_module.metrics				,	pl_module.metrics_save_path)
						return self._write_logs(_a				,	_a				,	"""test""")



	@rank_zero_only
	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    pl.Trainer				,	snake_case_							:    Dict)->							List[str]:
						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		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




