




"""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_rag import save_json







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  List[Any]								=       filter(lambda __UpperCamelCase	: p.requires_grad	,				model.parameters()	)
			__lowercase			:  int								=       sum([np.prod(p.size()	) for p in model_parameters]	)
			return params


a_        =       logging.getLogger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			if metric == "rouge2":
						__lowercase			:  str								=       '''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						__lowercase			:  int								=       '''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "em":
						__lowercase			:  List[Any]								=       '''{val_avg_em:.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.'''	)

			__lowercase			:  Tuple								=       ModelCheckpoint(
			    dirpath=_snake_case	,				filename=_snake_case	,				monitor=f"""val_{metric}"""	,				mode='''max'''	,				save_top_k=3	,				every_n_epochs=1	,				)
			return checkpoint_callback







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			return EarlyStopping(
			    monitor=f"""val_{metric}"""	,				mode='''min''' if '''loss''' in metric else '''max'''	,				patience=_snake_case	,				verbose=_snake_case	,				)


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

					if not save_generations:
								return

					if "preds" in metrics:
								__lowercase			:  Union[str, Any]								=       '''\n'''.join(metrics['''preds''']       )
								generations_file.open('''w+'''       ).write(__lowerCAmelCase       )
		@rank_zero_only
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       List[str]:
					try:
								__lowercase			:  Any								=       pl_module.model.model.num_parameters()
					except AttributeError:
								__lowercase			:  Any								=       pl_module.model.num_parameters()

					__lowercase			:  Dict								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Optional[int]:
					save_json(pl_module.metrics				,						pl_module.metrics_save_path       )
					return self._write_logs(__lowerCAmelCase				,						__lowerCAmelCase				,						'''test'''       )
		@rank_zero_only
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Dict:
					save_json(pl_module.metrics				,						pl_module.metrics_save_path       )
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")








from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



