


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      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->str:
   '''simple docstring'''






   snake_case_         =   filter(lambda lowerCAmelCase_							: p.requires_grad						,  model.parameters()							)
   snake_case_         =   sum([np.prod(p.size()							) for p in model_parameters]							)
   return params


SCREAMING_SNAKE_CASE		:Dict											=       logging.getLogger(__name__)


class       __lowerCAmelCase       (	pl.Callback   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    Optional[int]					)				->	str:




     """simple docstring"""
     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(_lowerCAmelCase					)



  @rank_zero_only
  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    pl.Trainer      ,			_lowerCAmelCase    :    pl.LightningModule      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    Any=True					)				->	None:




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

     if not save_generations:
        return

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



  @rank_zero_only
  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    List[Any]					)				->	List[str]:




     """simple docstring"""
     try:
        snake_case_         =   pl_module.model.model.num_parameters()
     except AttributeError:
        snake_case_         =   pl_module.model.num_parameters()

     snake_case_         =   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    :    List[str]      ,			_lowerCAmelCase    :    pl.Trainer      ,			_lowerCAmelCase    :    pl.LightningModule					)				->	str:




     """simple docstring"""
     save_json(pl_module.metrics      ,			pl_module.metrics_save_path					)
     return self._write_logs(_lowerCAmelCase      ,			_lowerCAmelCase      ,			"test"					)



  @rank_zero_only
  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    pl.Trainer      ,			_lowerCAmelCase    :    Optional[int]					)				->	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      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:List[str]=1						,  lowerCAmelCase_							:Union[str, Any]=False							)->List[Any]:
   '''simple docstring'''






   if metric == "rouge2":
      snake_case_         =   "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      snake_case_         =   "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
      snake_case_         =   "{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."							)

   snake_case_         =   ModelCheckpoint(
       dirpath=lowerCAmelCase_						,  filename=lowerCAmelCase_						,  monitor=F'''val_{metric}'''						,  mode="min" if "loss" in metric else "max"						,  save_top_k=lowerCAmelCase_						,  )
   return checkpoint_callback



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Optional[int]							)->Any:
   '''simple docstring'''






   return EarlyStopping(
       monitor=F'''val_{metric}'''						,  mode="min" if "loss" in metric else "max"						,  patience=lowerCAmelCase_						,  verbose=lowerCAmelCase_						,  )








from typing import Any

import numpy as np



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray							)->bool:
   '''simple docstring'''






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

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

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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->Any:
   '''simple docstring'''






   snake_case_         =   np.random.randn(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  random_matrix.T							)
   assert _is_matrix_spd(lowerCAmelCase_							)
   return spd_matrix



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:int = 1_000						,  lowerCAmelCase_							:float = 1e-8						,  )->Any:
   '''simple docstring'''






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

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

   # 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(lowerCAmelCase_						,  lowerCAmelCase_							)

      # The main algorithm.

      # Update search direction magnitude.
      snake_case_         =   np.dot(ra.T						,  lowerCAmelCase_							) / np.dot(pa.T						,  lowerCAmelCase_							)
      # 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						,  lowerCAmelCase_							) / np.dot(ra.T						,  lowerCAmelCase_							)
      # 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(lowerCAmelCase_						,  lowerCAmelCase_							)

      # Update variables.
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)

      # 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(lowerCAmelCase_							)
   snake_case_         =   np.random.randn(lowerCAmelCase_						,  1							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Numpy solution.
   snake_case_         =   np.linalg.solve(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Our implementation.
   snake_case_         =   conjugate_gradient(lowerCAmelCase_						,  lowerCAmelCase_							)

   # 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()




