

"""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      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					A    =      filter(lambda lowercase__   : p.requires_grad    ,							model.parameters()   )
					A    =      sum([np.prod(p.size()   ) for p in model_parameters]   )
					return params


__A  :					Optional[Any]												=	logging.getLogger(__name__)





class     __UpperCamelCase				(     pl.Callback		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
							pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=True):
							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=__SCREAMING_SNAKE_CASE)
												generations_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE)
							with open(__SCREAMING_SNAKE_CASE  ,  "a+") as writer:
												for key in sorted(__SCREAMING_SNAKE_CASE):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	A    =      metrics[key]
																	if isinstance(__SCREAMING_SNAKE_CASE  ,  torch.Tensor):
																						A    =      val.item()
																	A    =      F"""{key}: {val:.6f}\n"""
																	writer.write(__SCREAMING_SNAKE_CASE)

							if not save_generations:
												return

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


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
							try:
												A    =      pl_module.model.model.num_parameters()
							except AttributeError:
												A    =      pl_module.model.num_parameters()

							A    =      count_trainable_parameters(__SCREAMING_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       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule):
							save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
							return self._write_logs(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  "test")


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=1    ,							lowercase__=False   ):
					"""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=lowercase__    ,							filename=lowercase__    ,							monitor=F"""val_{metric}"""    ,							mode="min" if "loss" in metric else "max"    ,							save_top_k=lowercase__    ,							)
					return checkpoint_callback
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					return EarlyStopping(
					    monitor=F"""val_{metric}"""    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase__    ,							verbose=lowercase__    ,							)








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if not isinstance(lowercase__    ,							lowercase__   ):
										A    =      (
										    "operation can not be conducted on a object of type "
										    F"""{type(lowercase__   ).__name__}"""
										)
										raise TypeError(lowercase__   )
					A    =      ""
					for _ in range(0    ,							32   ):
										bit_string += str(number % 2   )
										A    =      number >> 1
					return bit_string
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if number < 0:
										raise ValueError("the value of input must be positive"   )
					elif isinstance(lowercase__    ,							lowercase__   ):
										raise TypeError("Input value must be a 'int' type"   )
					elif isinstance(lowercase__    ,							lowercase__   ):
										raise TypeError("'<' not supported between instances of 'str' and 'int'"   )
					A    =      0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1    ,							33   ):
										# left shift the bits by unity
										A    =      result << 1
										# get the end bit
										A    =      number % 2
										# right shift the bits by unity
										A    =      number >> 1
										# add that bit to our ans
										A    =      result | end_bit
					return get_reverse_bit_string(lowercase__   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





