



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


UpperCAmelCase_		 =					logging.getLogger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:Dict				)  ->     Any:
   if metric == "rouge2":
      _A      =       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A      =       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A      =       '''{val_avg_em:.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.'''				)

   _A      =       ModelCheckpoint(
       dirpath=_snake_case	,   filename=_snake_case	,   monitor=F'''val_{metric}'''	,   mode='''max'''	,   save_top_k=3	,   every_n_epochs=1	,   )
   return checkpoint_callback

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int	,   _snake_case	:Any				)  ->     Union[str, Any]:
   return EarlyStopping(
       monitor=F'''val_{metric}'''	,   mode='''min''' if '''loss''' in metric else '''max'''	,   patience=_snake_case	,   verbose=_snake_case	,   )


class 	lowerCamelCase__	(       pl.Callback):



 """simple docstring"""

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					List[Any]      )   ->	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(__lowerCAmelCase      )

 @rank_zero_only
 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					pl.Trainer			,						__lowerCAmelCase					:					pl.LightningModule			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[Any]=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=__lowerCAmelCase      )
       generations_file.parent.mkdir(exist_ok=__lowerCAmelCase      )
    with open(__lowerCAmelCase			,						'''a+'''      ) as writer:
       for key in sorted(__lowerCAmelCase      ):
          if key in ["log", "progress_bar", "preds"]:
             continue
          _A      =       metrics[key]
          if isinstance(__lowerCAmelCase			,						torch.Tensor      ):
             _A      =       val.item()
          _A      =       f'''{key}: {val:.6f}\n'''
          writer.write(__lowerCAmelCase      )

    if not save_generations:
       return

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

 @rank_zero_only
 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Any      )   ->	List[str]:
    try:
       _A      =       pl_module.model.model.num_parameters()
    except AttributeError:
       _A      =       pl_module.model.num_parameters()

    _A      =       count_trainable_parameters(__lowerCAmelCase      )
    # 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					:					Any			,						__lowerCAmelCase					:					pl.Trainer			,						__lowerCAmelCase					:					pl.LightningModule      )   ->	Optional[int]:
    save_json(pl_module.metrics			,						pl_module.metrics_save_path      )
    return self._write_logs(__lowerCAmelCase			,						__lowerCAmelCase			,						'''test'''      )

 @rank_zero_only
 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					pl.Trainer			,						__lowerCAmelCase					:					List[str]      )   ->	Dict:
    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 collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




