
import matplotlib.pyplot as plt
import numpy as np




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: float     )	->		np.ndarray:
				__lowercase								=   len(SCREAMING_SNAKE_CASE     )  # Number of training samples
				__lowercase								=   np.eye(SCREAMING_SNAKE_CASE     )  # Initialize weights as identity matrix
				for j in range(SCREAMING_SNAKE_CASE     ):
								__lowercase								=   point - x_train[j]
								__lowercase								=   np.exp(diff @ diff.T / (-2.0 * tau**2)     )

				return weights




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: float     )	->		np.ndarray:
				__lowercase								=   weight_matrix(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: float     )	->		np.ndarray:
				__lowercase								=   np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
				for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
								__lowercase								=   item @ local_weight(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

				return y_pred




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: str     )	->		tuple[np.ndarray, np.ndarray, np.ndarray]:
				import seaborn as sns

				__lowercase								=   sns.load_dataset(SCREAMING_SNAKE_CASE     )
				__lowercase								=   np.array(data[x_name]     )
				__lowercase								=   np.array(data[y_name]     )

				__lowercase								=   np.ones(len(SCREAMING_SNAKE_CASE     )     )

				# pairing elements of one and x_data
				__lowercase								=   np.column_stack((one, x_data)     )

				return x_train, x_data, y_data




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: str       ,				)	->		None:
				__lowercase								=   np.sort(SCREAMING_SNAKE_CASE       ,				axis=0     )
				plt.scatter(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				color='blue'     )
				plt.plot(
				    x_train_sorted[:, 1]       ,				preds[x_train[:, 1].argsort(0     )]       ,				color='yellow'       ,				linewidth=5       ,				)
				plt.title('Local Weighted Regression'     )
				plt.xlabel(SCREAMING_SNAKE_CASE     )
				plt.ylabel(SCREAMING_SNAKE_CASE     )
				plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__				=							load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__				=							local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")







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 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   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								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   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								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

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





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


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

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# 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]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


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





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



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

				__lowercase								=   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 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





