
"""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 						__magic_name__		(				UpperCamelCase    :		List[Any]    )    ->	List[Any]:
    a__    = filter(lambda UpperCamelCase    : p.requires_grad       ,		model.parameters()    )
    a__    = sum([np.prod(p.size()    ) for p in model_parameters]    )
    return params


a		:					List[Any]    	=					logging.getLogger(__name__)







class 						lowercase(pl.Callback					):



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """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(_lowerCAmelCase      )



     @rank_zero_only
     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=True      )		->    Optional[Any]:







         """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=_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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Tuple:







         """simple docstring"""



         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



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




     @rank_zero_only
     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    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 						__magic_name__		(				UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		str       ,		UpperCamelCase    :		Optional[int]=1       ,		UpperCamelCase    :		Union[str, Any]=False    )    ->	Tuple:
    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 						__magic_name__		(				UpperCamelCase    :		Optional[Any]       ,		UpperCamelCase    :		Any    )    ->	Tuple:
    return EarlyStopping(
        monitor=f'val_{metric}'       ,		mode='min' if 'loss' in metric else 'max'       ,		patience=__lowerCamelCase       ,		verbose=__lowerCamelCase       ,		)




"""simple docstring"""




import math







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = len(__SCREAMING_SNAKE_CASE      )
         a__    = [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1    ,							0    ,							self.N - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[Any]:







         """simple docstring"""



         return idx * 2



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         return idx * 2 + 1



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:  # noqa: E741







         """simple docstring"""



         if l == r:
             a__    = A[l]
         else:
             a__    = (l + r) // 2
             self.build(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             self.build(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE      )
             a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         return self.update_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return True
         if l == r:
             a__    = val
             return True
         a__    = (l + r) // 2
         self.update_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.update_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )
         return True



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         return self.query_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Any:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return -math.inf
         if l >= a and r <= b:
             return self.st[idx]
         a__    = (l + r) // 2
         a__    = self.query_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self.query_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         return max(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         a__    = []
         for i in range(1    ,							N + 1      ):
             show_list += [self.query(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )]
         print(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
    a		:					Union[str, Any]    	=					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    a		:					Dict    	=					15
    a		:					Tuple    	=					SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 1_11)
    print(segt.query(1, 15))
    segt.update(7, 8, 2_35)
    segt.show_data()


