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 					__lowercase						(    lowerCamelCase__       )					->str:

  '''simple docstring'''




  a__  		=  filter(lambda lowerCamelCase__       : p.requires_grad			,					model.parameters()       )
  a__  		=  sum([np.prod(p.size()       ) for p in model_parameters]       )
  return params


_SCREAMING_SNAKE_CASE     =     logging.getLogger(__name__)







class        UpperCAmelCase_					(				pl.Callback     ):




    def     __lowerCamelCase			(       self	: List[str]   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: List[str] )						->			Tuple:
      a__  		=  {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     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: pl.Trainer   ,   lowerCAmelCase	: pl.LightningModule   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: int=True )						->			None:
      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=_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
          a__  		=  metrics[key]
          if isinstance(_UpperCAmelCase   ,   torch.Tensor ):
            a__  		=  val.item()
          a__  		=  f"{key}: {val:.6f}\n"
          writer.write(_UpperCAmelCase )

      if not save_generations:
        return

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




    @rank_zero_only
    def     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: str )						->			Optional[int]:


      try:
        a__  		=  pl_module.model.model.num_parameters()
      except AttributeError:
        a__  		=  pl_module.model.num_parameters()

      a__  		=  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     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: pl.Trainer   ,   lowerCAmelCase	: pl.LightningModule )						->			Optional[Any]:
      save_json(pl_module.metrics   ,   pl_module.metrics_save_path )
      return self._write_logs(_UpperCAmelCase   ,   _UpperCAmelCase   ,   "test" )




    @rank_zero_only
    def     __lowerCamelCase			(       self	: int   ,   lowerCAmelCase	: pl.Trainer   ,   lowerCAmelCase	: Tuple )						->			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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=1			,					lowerCamelCase__=False       )					->List[str]:

  '''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=lowerCamelCase__			,					filename=lowerCamelCase__			,					monitor=f"val_{metric}"			,					mode="min" if "loss" in metric else "max"			,					save_top_k=lowerCamelCase__			,					)
  return checkpoint_callback







def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Union[str, Any]:

  '''simple docstring'''




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



def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





