


'''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__(						_UpperCamelCase					: List[str]      )						->    int:





   '''simple docstring'''







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


__lowercase:						Optional[int]  		=   logging.getLogger(__name__)







class        UpperCAmelCase       (				pl.Callback):

 def 						lowercase_  (       self       :		Tuple,    a_       :		Tuple,    a_       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  {f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
    pl_module.logger.log_metrics(a_    )

 @rank_zero_only
 def 						lowercase_  (       self       :		str,    a_       :		List[str],    a_       :		Tuple,    a_       :		Tuple,    a_       :		Any=True    ):





    """simple docstring"""



    logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'    )
    UpperCamelCase__         =  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__         =  Path(pl_module.hparams.output_dir    )
    if type_path == "test":
       UpperCamelCase__         =  od / "test_results.txt"
       UpperCamelCase__         =  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__         =  od / f'{type_path}_results/{trainer.global_step:05d}.txt'
       UpperCamelCase__         =  od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
       results_file.parent.mkdir(exist_ok=a_    )
       generations_file.parent.mkdir(exist_ok=a_    )
    with open(a_,    "a+"    ) as writer:
       for key in sorted(a_    ):
          if key in ["log", "progress_bar", "preds"]:
             continue
          UpperCamelCase__         =  metrics[key]
          if isinstance(a_,    torch.Tensor    ):
             UpperCamelCase__         =  val.item()
          UpperCamelCase__         =  f'{key}: {val:.6f}\n'
          writer.write(a_    )

    if not save_generations:
       return

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

 @rank_zero_only
 def 						lowercase_  (       self       :		Union[str, Any],    a_       :		Any,    a_       :		Any    ):





    """simple docstring"""



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

    UpperCamelCase__         =  count_trainable_parameters(a_    )
    # 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       :		Optional[int],    a_       :		Tuple,    a_       :		Optional[int]    ):





    """simple docstring"""



    save_json(pl_module.metrics,    pl_module.metrics_save_path    )
    return self._write_logs(a_,    a_,    "test"    )

 @rank_zero_only
 def 						lowercase_  (       self       :		Union[str, Any],    a_       :		int,    a_       :		Optional[Any]    ):





    """simple docstring"""



    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__(						_UpperCamelCase					: Tuple			,						_UpperCamelCase					: List[Any]			,						_UpperCamelCase					: Dict=1			,						_UpperCamelCase					: List[str]=False      )						->    Optional[int]:





   '''simple docstring'''







   if metric == "rouge2":
      UpperCamelCase__         =  "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      UpperCamelCase__         =  "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
      UpperCamelCase__         =  "{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__         =  ModelCheckpoint(
       dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=F'val_{metric}'			,						mode="min" if "loss" in metric else "max"			,						save_top_k=__UpperCamelCase			,						)
   return checkpoint_callback






def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Dict			,						_UpperCamelCase					: Dict      )						->    Union[str, Any]:





   '''simple docstring'''







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





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							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          a      =      {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
          pl_module.logger.log_metrics(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          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=A )
               generations_file.parent.mkdir(exist_ok=A )
          with open(A  ,		"a+" ) as writer:
               for key in sorted(A ):
                    if key in ["log", "progress_bar", "preds"]:
                         continue
                    a      =      metrics[key]
                    if isinstance(A  ,		torch.Tensor ):
                         a      =      val.item()
                    a      =      F'''{key}: {val:.6f}\n'''
                    writer.write(A )

          if not save_generations:
               return

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







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



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

          a      =      count_trainable_parameters(A )
          # 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  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          return self._write_logs(A  ,		A  ,		"test" )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          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							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     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=__UpperCamelCase		,					filename=__UpperCamelCase		,					monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					save_top_k=__UpperCamelCase		,					)
     return checkpoint_callback





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     return EarlyStopping(
         monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					patience=__UpperCamelCase		,					verbose=__UpperCamelCase		,					)

