code: 
  
  """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__    ,							)
  
  
  
  
  
  
style_context: 
  
  """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()
  
  
  
  
  
  