






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    _lowerCamelCase	(	__lowerCamelCase					)   -> List[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Dict										= filter(lambda __lowerCamelCase					: p.requires_grad   ,       model.parameters()					)
     UpperCAmelCase__			:     Dict										= sum([np.prod(p.size()					) for p in model_parameters]					)
     return params


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







class 							UpperCAmelCase_     (		pl.Callback		):




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     List[str]										= {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
          pl_module.logger.log_metrics(_lowerCAmelCase		)




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

          if not save_generations:
               return

          if "preds" in metrics:
               UpperCAmelCase__			:     List[Any]										= """\n""".join(metrics["""preds"""]		)
               generations_file.open("""w+"""		).write(_lowerCAmelCase		)




     @rank_zero_only
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          try:
               UpperCAmelCase__			:     Tuple										= pl_module.model.model.num_parameters()
          except AttributeError:
               UpperCAmelCase__			:     Dict										= pl_module.model.num_parameters()

          UpperCAmelCase__			:     Tuple										= count_trainable_parameters(_lowerCAmelCase		)
          # 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  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          save_json(pl_module.metrics  ,	pl_module.metrics_save_path		)
          return self._write_logs(_lowerCAmelCase  ,	_lowerCAmelCase  ,	"""test"""		)




     @rank_zero_only
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          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   ,       __lowerCamelCase   ,       __lowerCamelCase=1   ,       __lowerCamelCase=False					)   -> Tuple:

     '''simple docstring'''
     if metric == "rouge2":
          UpperCAmelCase__			:     int										= """{val_avg_rouge2:.4f}-{step_count}"""
     elif metric == "bleu":
          UpperCAmelCase__			:     Tuple										= """{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=__lowerCamelCase   ,       filename=__lowerCamelCase   ,       monitor=F"val_{metric}"   ,       mode="""min""" if """loss""" in metric else """max"""   ,       save_top_k=__lowerCamelCase   ,       )
     return checkpoint_callback







def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Tuple:

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














import random







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Union[str, Any]										= x
          UpperCAmelCase__			:     Union[str, Any]										= y




     def      __UpperCAmelCase     (							self		):
          return (self.x**2 + self.y**2) <= 1




     @classmethod
     def      __UpperCAmelCase     (							cls		):
          return cls(x=random.random()  ,	y=random.random()		)





def    _lowerCamelCase	(	__lowerCamelCase					)   -> float:

     '''simple docstring'''
     if number_of_simulations < 1:
          raise ValueError("""At least one simulation is necessary to estimate PI."""					)

     UpperCAmelCase__			:     Dict										= 0
     for _ in range(__lowerCamelCase					):
          UpperCAmelCase__			:     Union[str, Any]										= Point.random_unit_square()

          if random_point.is_in_unit_circle():
               number_in_unit_circle += 1

     return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
      # import doctest

      # doctest.testmod()
      from math import pi

      SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""Please enter the desired number of Monte Carlo simulations: """
      SCREAMING_SNAKE_CASE__		:       int   							=							estimate_pi(int(input(prompt).strip()))
      print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






