

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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_          =		filter(lambda SCREAMING_SNAKE_CASE_       : p.requires_grad       ,		model.parameters()       )
    UpperCamelCase_          =		sum([np.prod(p.size()       ) for p in model_parameters]       )
    return params


SCREAMING_SNAKE_CASE						:Any      =			logging.getLogger(__name__)






class 		__magic_name__ (				pl.Callback							):






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			int:
         UpperCamelCase_          =		{F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
         pl_module.logger.log_metrics(_lowercase     )






     @rank_zero_only
     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase=True     )->			None:
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"     )
         UpperCamelCase_          =		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
         UpperCamelCase_          =		Path(pl_module.hparams.output_dir     )
         if type_path == "test":
             UpperCamelCase_          =		od / "test_results.txt"
             UpperCamelCase_          =		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.
             UpperCamelCase_          =		od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             UpperCamelCase_          =		od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=_lowercase     )
             generations_file.parent.mkdir(exist_ok=_lowercase     )
         with open(_lowercase						,		"a+"     ) as writer:
             for key in sorted(_lowercase     ):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 UpperCamelCase_          =		metrics[key]
                 if isinstance(_lowercase						,		torch.Tensor     ):
                     UpperCamelCase_          =		val.item()
                 UpperCamelCase_          =		F"{key}: {val:.6f}\n"
                 writer.write(_lowercase     )

         if not save_generations:
             return

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






     @rank_zero_only
     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			str:
         try:
             UpperCamelCase_          =		pl_module.model.model.num_parameters()
         except AttributeError:
             UpperCamelCase_          =		pl_module.model.num_parameters()

         UpperCamelCase_          =		count_trainable_parameters(_lowercase     )
         # 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						,		_lowercase						,		_lowercase     )->			Optional[int]:
         save_json(pl_module.metrics						,		pl_module.metrics_save_path     )
         return self._write_logs(_lowercase						,		_lowercase						,		"test"     )






     @rank_zero_only
     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			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")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=1       ,		SCREAMING_SNAKE_CASE_=False       )->		Optional[int]:



    """simple docstring"""






    if metric == "rouge2":
        UpperCamelCase_          =		"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        UpperCamelCase_          =		"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "loss":
        UpperCamelCase_          =		"{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."       )

    UpperCamelCase_          =		ModelCheckpoint(
        dirpath=SCREAMING_SNAKE_CASE_       ,		filename=SCREAMING_SNAKE_CASE_       ,		monitor=f"val_{metric}"       ,		mode="min" if "loss" in metric else "max"       ,		save_top_k=SCREAMING_SNAKE_CASE_       ,		)
    return checkpoint_callback




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    return EarlyStopping(
        monitor=f"val_{metric}"       ,		mode="min" if "loss" in metric else "max"       ,		patience=SCREAMING_SNAKE_CASE_       ,		verbose=SCREAMING_SNAKE_CASE_       ,		)



from __future__ import annotations

from queue import Queue




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple[int, int]:



    """simple docstring"""






    a ^= b
    b ^= a
    a ^= b
    return a, b




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list[list[int]]:



    """simple docstring"""






    UpperCamelCase_          =		1
    while (1 << j) < max_node:
        for i in range(1       ,		max_node + 1       ):
            UpperCamelCase_          =		parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    if level[u] < level[v]:
        UpperCamelCase_   ,    UpperCamelCase_          =		swap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    # making depth of u same as depth of v
    for i in range(1_8       ,		-1       ,		-1       ):
        if level[u] - (1 << i) >= level[v]:
            UpperCamelCase_          =		parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(1_8       ,		-1       ,		-1       ):
        if parent[i][u] not in [0, parent[i][v]]:
            UpperCamelCase_   ,    UpperCamelCase_          =		parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       ,		)->		tuple[list[int], list[list[int]]]:



    """simple docstring"""






    UpperCamelCase_          =		0
    UpperCamelCase_          =		Queue(maxsize=SCREAMING_SNAKE_CASE_       )
    q.put(SCREAMING_SNAKE_CASE_       )
    while q.qsize() != 0:
        UpperCamelCase_          =		q.get()
        for v in graph[u]:
            if level[v] == -1:
                UpperCamelCase_          =		level[u] + 1
                q.put(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase_          =		u
    return level, parent




def        lowerCAmelCase(	)->		None:



    """simple docstring"""






    UpperCamelCase_          =		1_3
    # initializing with 0
    UpperCamelCase_          =		[[0 for _ in range(max_node + 1_0       )] for _ in range(2_0       )]
    # initializing with -1 which means every node is unvisited
    UpperCamelCase_          =		[-1 for _ in range(max_node + 1_0       )]
    UpperCamelCase_          =		{
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 1_0],
        6: [1_1],
        7: [],
        8: [1_2, 1_3],
        9: [],
        1_0: [],
        1_1: [],
        1_2: [],
        1_3: [],
    }
    UpperCamelCase_   ,    UpperCamelCase_          =		breadth_first_search(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		1       )
    UpperCamelCase_          =		create_sparse(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("LCA of node 1 and 3 is: "       ,		lowest_common_ancestor(1       ,		3       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 5 and 6 is: "       ,		lowest_common_ancestor(5       ,		6       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 7 and 11 is: "       ,		lowest_common_ancestor(7       ,		1_1       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 6 and 7 is: "       ,		lowest_common_ancestor(6       ,		7       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 4 and 12 is: "       ,		lowest_common_ancestor(4       ,		1_2       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 8 and 8 is: "       ,		lowest_common_ancestor(8       ,		8       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )


if __name__ == "__main__":
 main()
