






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


UpperCAmelCase__            =	logging.getLogger(__name__)






class 	lowercase_      (			pl.Callback     ):






      '''simple docstring'''




      def    __lowerCAmelCase      (   self   :		Dict				,      __UpperCAmelCase   :		Tuple				,      __UpperCAmelCase   :		List[Any]						)					->Union[str, Any]:




       """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(__UpperCAmelCase						)







      @rank_zero_only
      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      __UpperCAmelCase   :		pl.Trainer				,      __UpperCAmelCase   :		pl.LightningModule				,      __UpperCAmelCase   :		str				,      __UpperCAmelCase   :		str=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=__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   :		Optional[Any]				,      __UpperCAmelCase   :		List[Any]				,      __UpperCAmelCase   :		int						)					->Union[str, Any]:




       """simple docstring"""


       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   :		List[str]				,      __UpperCAmelCase   :		pl.Trainer				,      __UpperCAmelCase   :		pl.LightningModule						)					->Union[str, Any]:




       """simple docstring"""


       save_json(pl_module.metrics				,      pl_module.metrics_save_path						)
       return self._write_logs(__UpperCAmelCase				,      __UpperCAmelCase				,      '''test'''						)







      @rank_zero_only
      def    __lowerCAmelCase      (   self   :		Optional[int]				,      __UpperCAmelCase   :		pl.Trainer				,      __UpperCAmelCase   :		Any						)					->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 		_a					(   a				:Dict			,							a				:Optional[Any]			,							a				:Tuple=1			,							a				:Optional[Any]=False				)							->    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=a			,							filename=a			,							monitor=F"""val_{metric}"""			,							mode='''min''' if '''loss''' in metric else '''max'''			,							save_top_k=a			,							)
 return checkpoint_callback

def 		_a					(   a				:str			,							a				:Optional[int]				)							->    Optional[int]:
 return EarlyStopping(
     monitor=F"""val_{metric}"""			,							mode='''min''' if '''loss''' in metric else '''max'''			,							patience=a			,							verbose=a			,							)










def 		_a					(   a				:float				)							->    float:
 return -num if num < 0 else num
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_min() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs_val(a				) < abs_val(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs(a				) > abs(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max_sort() arg is an empty sequence'''				)
 return sorted(a			,							key=a				)[-1]
def 		_a					(   )							->    int:
 assert abs_val(0				) == 0
 assert abs_val(34				) == 34
 assert abs_val(-100_000_000_000				) == 100_000_000_000

 a							=   [-3, -1, 2, -11]
 assert abs_max(a				) == -11
 assert abs_max_sort(a				) == -11
 assert abs_min(a				) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34


