
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__		:Optional[int] 		=    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					:							int           =							logging.getLogger(__name__)


class 	UpperCamelCase__       (  pl.Callback):

						'''simple docstring'''



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

									if not save_generations:
												return

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


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

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


						@rank_zero_only
						def    A__       (  self  , A  , A	)  ->List[str]:
									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__		:str 		=    '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						UpperCAmelCase__		:Dict 		=    '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "loss":
						UpperCAmelCase__		:Any 		=    '{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__		:List[str] 		=    ModelCheckpoint(
			    dirpath=UpperCamelCase__     ,						filename=UpperCamelCase__     ,						monitor=f"""val_{metric}"""     ,						mode='min' if 'loss' in metric else 'max'     ,						save_top_k=UpperCamelCase__     ,						)
			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=UpperCamelCase__     ,						verbose=UpperCamelCase__     ,						)












from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(UpperCamelCase__							,     matrix.T    ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					_snake_case				,  _snake_case						=					np.linalg.eigh(UpperCamelCase__    )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]
					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[0]
					assert _is_matrix_spd(UpperCamelCase__    )

					# Initialize solution guess, residual, search direction.
					_snake_case						=					np.zeros((np.shape(UpperCamelCase__    )[0], 1)    )
					_snake_case						=					np.copy(UpperCamelCase__    )
					_snake_case						=					np.copy(UpperCamelCase__    )

					# Set initial errors in solution guess and residual.
					_snake_case						=					1e9
					_snake_case						=					1e9
					_snake_case						=					1e9

					# Set iteration counter to threshold number of iterations.
					_snake_case						=					0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

										# The main algorithm.

										# Update search direction magnitude.
										_snake_case						=					np.dot(ra.T							,     UpperCamelCase__    ) / np.dot(pa.T							,     UpperCamelCase__    )
										# Update solution guess.
										_snake_case						=					xa + alpha * pa
										# Calculate new residual.
										_snake_case						=					ra - alpha * w
										# Calculate new Krylov subspace scale.
										_snake_case						=					np.dot(r.T							,     UpperCamelCase__    ) / np.dot(ra.T							,     UpperCamelCase__    )
										# Calculate new A conjuage search direction.
										_snake_case						=					r + beta * pa

										# Calculate errors.
										_snake_case						=					np.linalg.norm(r - ra    )
										_snake_case						=					np.linalg.norm(x - xa    )
										_snake_case						=					np.maximum(UpperCamelCase__							,     UpperCamelCase__    )

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true    ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true    ) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()



