





'''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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  _UpperCamelCase       			=ModelCheckpoint(
      dirpath=_A  ,      filename=_A  ,      monitor=f'''val_{metric}'''  ,      mode='''max'''  ,      save_top_k=1  ,      every_n_epochs=1  ,      )

  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=_A  ,      verbose=_A  ,      )







class 		UpperCAmelCase   (				pl.Callback):


  """simple docstring"""







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

    if not save_generations:
      return

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


  @rank_zero_only
  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					List[Any]  )     ->						List[str]:






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

    _UpperCamelCase       			=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   UpperCamelCase__      (				self    :					Optional[int]							,	UpperCamelCase__    :					pl.Trainer							,	UpperCamelCase__    :					pl.LightningModule  )     ->						Optional[Any]:
    save_json(pl_module.metrics							,	pl_module.metrics_save_path  )
    return self._write_logs(__UpperCamelCase							,	__UpperCamelCase							,	'''test'''  )




  @rank_zero_only
  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					pl.Trainer							,	UpperCamelCase__    :					Any  )     ->						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")













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=cva.getAffineTransform(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return cva.warpAffine(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      (rows, cols)   )


if __name__ == "__main__":
      # read original image
      __lowerCamelCase						: List[Any]		=   cva.imread(
          str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
      )
      # turn image in gray scale value
      __lowerCamelCase						: List[Any]		=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      __lowerCamelCase		, __lowerCamelCase						: Any		=   gray_img.shape

      # set different points to rotate image
      __lowerCamelCase						: Optional[Any]		=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
      __lowerCamelCase						: Any		=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
      __lowerCamelCase						: int		=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
      __lowerCamelCase						: List[Any]		=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

      # add all rotated images in a list
      __lowerCamelCase						: int		=   [
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      __lowerCamelCase						: Optional[Any]		=   plt.figure(1)
      __lowerCamelCase						: Tuple		=   ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
            plt.title(titles[i])
            plt.axis('off')
            plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
      plt.show()






