
'''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_rag import save_json






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
       _snake_case       =  filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad							, model.parameters()				)
       _snake_case       =  sum([np.prod(p.size()				) for p in model_parameters]				)
       return params


__lowerCAmelCase												=	logging.getLogger(__name__)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       if metric == "rouge2":
              _snake_case       =  """{val_avg_rouge2:.4f}-{step_count}"""
       elif metric == "bleu":
              _snake_case       =  """{val_avg_bleu:.4f}-{step_count}"""
       elif metric == "em":
              _snake_case       =  """{val_avg_em:.4f}-{step_count}"""
       elif metric == "loss":
              _snake_case       =  """{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."""				)

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

       return checkpoint_callback






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       return EarlyStopping(
           monitor=f"""val_{metric}"""							, mode="""min""" if """loss""" in metric else """max"""							, patience=_SCREAMING_SNAKE_CASE							, verbose=_SCREAMING_SNAKE_CASE							, )


class       _lowerCAmelCase   (   pl.Callback				):







       '''simple docstring'''

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[Any]:
              _snake_case       =  {f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
              pl_module.logger.log_metrics(UpperCAmelCase  )

       @rank_zero_only
       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=True  )					->       None:
              logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""  )
              _snake_case       =  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
              _snake_case       =  Path(pl_module.hparams.output_dir  )
              if type_path == "test":
                     _snake_case       =  od / """test_results.txt"""
                     _snake_case       =  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.
                     _snake_case       =  od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                     _snake_case       =  od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                     results_file.parent.mkdir(exist_ok=UpperCAmelCase  )
                     generations_file.parent.mkdir(exist_ok=UpperCAmelCase  )
              with open(UpperCAmelCase  ,  """a+"""  ) as writer:
                     for key in sorted(UpperCAmelCase  ):
                            if key in ["log", "progress_bar", "preds"]:
                                   continue
                            _snake_case       =  metrics[key]
                            if isinstance(UpperCAmelCase  ,  torch.Tensor  ):
                                   _snake_case       =  val.item()
                            _snake_case       =  f"""{key}: {val:.6f}\n"""
                            writer.write(UpperCAmelCase  )

              if not save_generations:
                     return

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

       @rank_zero_only
       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
              try:
                     _snake_case       =  pl_module.model.model.num_parameters()
              except AttributeError:
                     _snake_case       =  pl_module.model.num_parameters()

              _snake_case       =  count_trainable_parameters(UpperCAmelCase  )
              # 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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
              save_json(pl_module.metrics  ,  pl_module.metrics_save_path  )
              return self._write_logs(UpperCAmelCase  ,  UpperCAmelCase  ,  """test"""  )





       @rank_zero_only
       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
              save_json(pl_module.metrics  ,  pl_module.metrics_save_path  )
              # Uncommenting this will save val generations
              # return self._write_logs(trainer, pl_module, "valid")

'''simple docstring'''
from __future__ import annotations

import typing
from collections import Counter






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  Counter()
							for base in range(1							, max_perimeter + 1				):
														for perpendicular in range(_SCREAMING_SNAKE_CASE							, max_perimeter + 1				):
																					_snake_case       =  (base * base + perpendicular * perpendicular) ** 0.5
																					if hypotenuse == int(_SCREAMING_SNAKE_CASE				):
																												_snake_case       =  int(base + perpendicular + hypotenuse				)
																												if perimeter > max_perimeter:
																																			continue
																												triplets[perimeter] += 1
							return triplets






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = 1000				):
							_snake_case       =  pythagorean_triple(_SCREAMING_SNAKE_CASE				)
							return triplets.most_common(1				)[0][0]


if __name__ == "__main__":
				print(f'''Perimeter {solution()} has maximum solutions''')