code: 
  
  
  
  
  
  
  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   ,       )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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)}''')
  
  
  
  
  
  
  