


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_rag import save_json
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]		 =						filter(lambda SCREAMING_SNAKE_CASE__						: p.requires_grad      ,	model.parameters()						)
 __lowerCamelCase		:							Union[str, Any]		 =						sum([np.prod(p.size()						) for p in model_parameters]						)
 return params


lowercase_	   =     logging.getLogger(__name__)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if metric == "rouge2":
  __lowerCamelCase		:							Tuple		 =						'{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  __lowerCamelCase		:							List[str]		 =						'{val_avg_bleu:.4f}-{step_count}'
 elif metric == "em":
  __lowerCamelCase		:							int		 =						'{val_avg_em:.4f}-{step_count}'
 elif metric == "loss":
  __lowerCamelCase		:							Optional[Any]		 =						'{val_avg_loss:.4f}-{step_count}'
 else:
  raise NotImplementedError(
      f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
      ' function.'						)

 __lowerCamelCase		:							List[Any]		 =						ModelCheckpoint(
     dirpath=SCREAMING_SNAKE_CASE__      ,	filename=SCREAMING_SNAKE_CASE__      ,	monitor=f'val_{metric}'      ,	mode='max'      ,	save_top_k=1      ,	every_n_epochs=1      ,	)

 return checkpoint_callback
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return EarlyStopping(
     monitor=f'val_{metric}'      ,	mode='min' if 'loss' in metric else 'max'      ,	patience=SCREAMING_SNAKE_CASE__      ,	verbose=SCREAMING_SNAKE_CASE__      ,	)



class 	A_ (	pl.Callback   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Optional[Any]						,				a:	Optional[int]						,				a:	int    ):
       __lowerCamelCase		:							str		 =						{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 					_snake_case     (    self:	Dict						,				a:	pl.Trainer						,				a:	pl.LightningModule						,				a:	str						,				a:	str=True    ):
       logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'    )
       __lowerCamelCase		:							Tuple		 =						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
       __lowerCamelCase		:							List[Any]		 =						Path(pl_module.hparams.output_dir    )
       if type_path == "test":
        __lowerCamelCase		:							List[str]		 =						od / 'test_results.txt'
        __lowerCamelCase		:							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.
        __lowerCamelCase		:							Any		 =						od / F'{type_path}_results/{trainer.global_step:05d}.txt'
        __lowerCamelCase		:							Dict		 =						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
         __lowerCamelCase		:							str		 =						metrics[key]
         if isinstance(a						,				torch.Tensor    ):
          __lowerCamelCase		:							Optional[int]		 =						val.item()
         __lowerCamelCase		:							int		 =						F'{key}: {val:.6f}\n'
         writer.write(a    )

       if not save_generations:
        return

       if "preds" in metrics:
        __lowerCamelCase		:							Union[str, Any]		 =						'\n'.join(metrics['preds']    )
        generations_file.open('w+'    ).write(a    )




      @rank_zero_only
      def 					_snake_case     (    self:	str						,				a:	Tuple						,				a:	Dict    ):
       try:
        __lowerCamelCase		:							str		 =						pl_module.model.model.num_parameters()
       except AttributeError:
        __lowerCamelCase		:							List[Any]		 =						pl_module.model.num_parameters()

       __lowerCamelCase		:							Tuple		 =						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 					_snake_case     (    self:	List[Any]						,				a:	pl.Trainer						,				a:	pl.LightningModule    ):
       save_json(pl_module.metrics						,				pl_module.metrics_save_path    )
       return self._write_logs(a						,				a						,				'test'    )




      @rank_zero_only
      def 					_snake_case     (    self:	str						,				a:	pl.Trainer						,				a:	List[Any]    ):
       save_json(pl_module.metrics						,				pl_module.metrics_save_path    )
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")





from math import pow
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	):
 if current_sum == needed_sum:
  # If the sum of the powers is equal to needed_sum, then we have a solution.
  solutions_count += 1
  return current_sum, solutions_count

 __lowerCamelCase		:							Optional[Any]		 =						int(pow(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
 if current_sum + i_to_n <= needed_sum:
  # If the sum of the powers is less than needed_sum, then continue adding powers.
  current_sum += i_to_n
  __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  current_sum -= i_to_n
 if i_to_n < needed_sum:
  # If the power of i is less than needed_sum, then try with the next power.
  __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return current_sum, solutions_count
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
  raise ValueError(
      'Invalid input\n'
      'needed_sum must be between 1 and 1000, power between 2 and 10.'						)

 return backtrack(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1      ,	0      ,	0						)[1]  # Return the solutions_count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

