






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


UpperCamelCase_						:					List[str]      				=			logging.getLogger(__name__)


class   _lowercase			(  pl.Callback    ):


      def _UpperCamelCase    (     self					:						Union[str, Any]				, a					:						List[str]				, a					:						Any    ):





            """simple docstring"""







            __snake_case   :     Any	      ={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 _UpperCamelCase    (     self					:						List[Any]				, a					:						pl.Trainer				, a					:						pl.LightningModule				, a					:						str				, a					:						Tuple=True    ):





            """simple docstring"""







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

            if not save_generations:
                  return

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


      @rank_zero_only
      def _UpperCamelCase    (     self					:						Union[str, Any]				, a					:						Dict				, a					:						str    ):





            """simple docstring"""







            try:
                  __snake_case   :     Optional[Any]	      =pl_module.model.model.num_parameters()
            except AttributeError:
                  __snake_case   :     List[Any]	      =pl_module.model.num_parameters()

            __snake_case   :     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 _UpperCamelCase    (     self					:						Dict				, a					:						pl.Trainer				, a					:						pl.LightningModule    ):





            """simple docstring"""







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







      @rank_zero_only
      def _UpperCamelCase    (     self					:						Optional[int]				, a					:						pl.Trainer				, a					:						Tuple    ):





            """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      __lowercase       (     a			:					Optional[Any]							,		a			:					Union[str, Any]							,		a			:					int=1							,		a			:					List[Any]=False			)       ->       Optional[int]:
      if metric == "rouge2":
            __snake_case   :     Dict	      ='''{val_avg_rouge2:.4f}-{step_count}'''
      elif metric == "bleu":
            __snake_case   :     List[str]	      ='''{val_avg_bleu:.4f}-{step_count}'''
      elif metric == "loss":
            __snake_case   :     Optional[Any]	      ='''{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.'''			)

      __snake_case   :     List[str]	      =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      __lowercase       (     a			:					List[str]							,		a			:					Optional[int]			)       ->       int:
      return EarlyStopping(
          monitor=f'''val_{metric}'''							,		mode='''min''' if '''loss''' in metric else '''max'''							,		patience=a							,		verbose=a							,		)










"""simple docstring"""





from __future__ import annotations


class   _lowercase			:


      def __init__(     self					:						int				, a					:						int = 0    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =value
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None



def      __lowercase       (     a			:					Node | None							,		a			:					Node | None			)       ->       Node | None:
      if treea is None:
            return treea
      if treea is None:
            return treea

      __snake_case   :     Dict	      =treea.value + treea.value
      __snake_case   :     int	      =merge_two_binary_trees(treea.left							,		treea.left			)
      __snake_case   :     List[str]	      =merge_two_binary_trees(treea.right							,		treea.right			)
      return treea



def      __lowercase       (     a			:					Node | None			)       ->       None:
      if root:
            print(root.value			)
            print_preorder(root.left			)
            print_preorder(root.right			)


if __name__ == "__main__":
    UpperCamelCase_						:					List[str]      				=			Node(1)
    UpperCamelCase_						:					List[Any]      				=			Node(2)
    UpperCamelCase_						:					Optional[Any]      				=			Node(3)
    UpperCamelCase_						:					int      				=			Node(4)

    UpperCamelCase_						:					int      				=			Node(2)
    UpperCamelCase_						:					List[str]      				=			Node(4)
    UpperCamelCase_						:					Any      				=			Node(6)
    UpperCamelCase_						:					Union[str, Any]      				=			Node(9)
    UpperCamelCase_						:					Dict      				=			Node(5)

    print("""Tree1 is: """)
    print_preorder(treea)
    print("""Tree2 is: """)
    print_preorder(treea)
    UpperCamelCase_						:					Optional[Any]      				=			merge_two_binary_trees(treea, treea)
    print("""Merged Tree is: """)
    print_preorder(merged_tree)


