
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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    filter(lambda SCREAMING_SNAKE_CASE							: p.requires_grad     ,						model.parameters()							)
			UpperCAmelCase__		:Optional[int] 		=    sum([np.prod(p.size()							) for p in model_parameters]							)
			return params


__snake_case					:							Optional[Any]           =							logging.getLogger(__name__)


class 	UpperCamelCase__       (  pl.Callback):

						'''simple docstring'''



						def    A__       (  self  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:List[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    A__       (  self  , A  , A  , A  , A=True	)  ->None:
									logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
									UpperCAmelCase__		:Optional[int] 		=    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__		:Tuple 		=    Path(pl_module.hparams.output_dir	)
									if type_path == "test":
												UpperCAmelCase__		:str 		=    od / 'test_results.txt'
												UpperCAmelCase__		:Union[str, Any] 		=    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__		:List[str] 		=    od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
												UpperCAmelCase__		:str 		=    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
															UpperCAmelCase__		:int 		=    metrics[key]
															if isinstance(A  , torch.Tensor	):
																		UpperCAmelCase__		:int 		=    val.item()
															UpperCAmelCase__		:Tuple 		=    F"""{key}: {val:.6f}\n"""
															writer.write(A	)

									if not save_generations:
												return

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


						@rank_zero_only
						def    A__       (  self  , A  , A	)  ->Union[str, Any]:
									try:
												UpperCAmelCase__		:Optional[Any] 		=    pl_module.model.model.num_parameters()
									except AttributeError:
												UpperCAmelCase__		:List[str] 		=    pl_module.model.num_parameters()

									UpperCAmelCase__		:Optional[int] 		=    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    A__       (  self  , A  , A	)  ->List[str]:
									save_json(pl_module.metrics  , pl_module.metrics_save_path	)
									return self._write_logs(A  , A  , 'test'	)


						@rank_zero_only
						def    A__       (  self  , A  , A	)  ->Union[str, 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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=1     ,						SCREAMING_SNAKE_CASE=False							):




			"""simple docstring"""


			if metric == "rouge2":
						UpperCAmelCase__		:Optional[int] 		=    '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						UpperCAmelCase__		:Any 		=    '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "loss":
						UpperCAmelCase__		:List[str] 		=    '{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__		:Dict 		=    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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""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


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self	)  ->None:
									UpperCAmelCase__		:dict[int, list[int]] 		=    {}


						def    A__       (  self	)  ->None:
									for i in self.vertices:
												print(A  , ' : '  , ' -> '.join([str(A	) for j in self.vertices[i]]	)	)


						def    A__       (  self  , A  , A	)  ->None:
									if from_vertex in self.vertices:
												self.vertices[from_vertex].append(A	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    [to_vertex]


						def    A__       (  self  , A	)  ->set[int]:
									UpperCAmelCase__		:str 		=    set()

									# create a first in first out queue to store all the vertices for BFS
									UpperCAmelCase__		:Queue 		=    Queue()

									# mark the source node as visited and enqueue it
									visited.add(A	)
									queue.put(A	)

									while not queue.empty():
												UpperCAmelCase__		:List[str] 		=    queue.get()

												# loop through all adjacent vertex and enqueue it if not yet visited
												for adjacent_vertex in self.vertices[vertex]:
															if adjacent_vertex not in visited:
																		queue.put(A	)
																		visited.add(A	)
									return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						__snake_case					:							Any           =							Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]





