




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    (							UpperCAmelCase_	:      Dict      )->			Optional[int]:

       """simple docstring"""




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


_lowerCamelCase							=      logging.getLogger(__name__)


class        UpperCAmelCase__				(							pl.Callback				):



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

            if not save_generations:
                   return

            if "preds" in metrics:
                   a			   ="""\n""".join(metrics["""preds"""]	)
                   generations_file.open("""w+"""	).write(_lowerCAmelCase	)
     @rank_zero_only
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            try:
                   a			   =pl_module.model.model.num_parameters()
            except AttributeError:
                   a			   =pl_module.model.num_parameters()

            a			   =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 ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            save_json(pl_module.metrics ,    pl_module.metrics_save_path	)
            return self._write_logs(_lowerCAmelCase ,    _lowerCAmelCase ,    """test"""	)






     @rank_zero_only
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            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    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      int=1   ,       UpperCAmelCase_	:      List[Any]=False      )->			str:

       """simple docstring"""




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

       a			   =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 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      Any      )->			Union[str, Any]:

       """simple docstring"""




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






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
