




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


SCREAMING_SNAKE_CASE:     Union[str, Any]									= logging.getLogger(__name__)


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Any:







		if metric == "rouge2":
				SCREAMING_SNAKE_CASE_					      =							'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				SCREAMING_SNAKE_CASE_					      =							'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				SCREAMING_SNAKE_CASE_					      =							'{val_avg_em:.4f}-{step_count}'
		elif metric == "loss":
				SCREAMING_SNAKE_CASE_					      =							'{val_avg_loss:.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.' )

		SCREAMING_SNAKE_CASE_					      =							ModelCheckpoint(
		    dirpath=UpperCamelCase__    ,						filename=UpperCamelCase__    ,						monitor=F'''val_{metric}'''    ,						mode='max'    ,						save_top_k=1    ,						every_n_epochs=1    ,						)

		return checkpoint_callback


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Tuple:
		return EarlyStopping(
		    monitor=F'''val_{metric}'''    ,						mode='min' if 'loss' in metric else 'max'    ,						patience=UpperCamelCase__    ,						verbose=UpperCamelCase__    ,						)




class    lowercase_		(pl.Callback ):



						def 	__a					(			self	:   int					,							snake_case__	:   Any					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{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	:   Union[str, Any]					,							snake_case__	:   List[str]					,							snake_case__	:   Dict					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   Dict=True  ):

								"""simple docstring"""

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

								if not save_generations:
										return

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



						@rank_zero_only
						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								try:
										SCREAMING_SNAKE_CASE_					      =							pl_module.model.model.num_parameters()
								except AttributeError:
										SCREAMING_SNAKE_CASE_					      =							pl_module.model.num_parameters()

								SCREAMING_SNAKE_CASE_					      =							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	:   Union[str, Any]					,							snake_case__	:   int					,							snake_case__	:   Dict  ):

								"""simple docstring"""

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






						@rank_zero_only
						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Dict					,							snake_case__	:   List[Any]  ):

								"""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 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	return [
	    {
	        0: [1, 2],
	        1: [0, 2],
	        2: [0, 1, 3, 5],
	        3: [2, 4],
	        4: [3],
	        5: [2, 6, 8],
	        6: [5, 7],
	        7: [6, 8],
	        8: [5, 7],
	    },
	    {
	        0: [6],
	        1: [9],
	        2: [4, 5],
	        3: [4],
	        4: [2, 3],
	        5: [2],
	        6: [0, 7],
	        7: [6],
	        8: [],
	        9: [1],
	    },
	    {
	        0: [4],
	        1: [6],
	        2: [],
	        3: [5, 6, 7],
	        4: [0, 6],
	        5: [3, 8, 9],
	        6: [1, 3, 4, 7],
	        7: [3, 6, 8, 9],
	        8: [5, 7],
	        9: [5, 7],
	    },
	    {
	        0: [1, 3],
	        1: [0, 2, 4],
	        2: [1, 3, 4],
	        3: [0, 2, 4],
	        4: [1, 2, 3],
	    },
	][index]





def 	_lowerCAmelCase					( UpperCamelCase__:  dict[int, list[int]]    )			-> list[tuple[int, int]]:





	"""simple docstring"""







	A             =					0
	A             =					len(UpperCamelCase__    )  # No of vertices in graph
	A             =					[0] * n
	A             =					[False] * n

	def dfs(UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Any    ):
		A             =					True
		A             =					id_
		id_ += 1
		for to in graph[at]:
			if to == parent:
				pass
			elif not visited[to]:
				dfs(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       id_    )
				A             =					min(low[at]				,       low[to]    )
				if id_ <= low[to]:
					bridges.append((at, to) if at < to else (to, at)    )
			else:
				# This edge is a back edge and cannot be a bridge
				A             =					min(low[at]				,       low[to]    )

	A             =					[]
	for i in range(UpperCamelCase__    ):
		if not visited[i]:
			dfs(UpperCamelCase__				,       -1				,       UpperCamelCase__				,       id_    )
	return bridges


if __name__ == "__main__":
				import doctest

				doctest.testmod()



