
import copy
import os

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






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Dict					=       ''''''
      _A:  Optional[int]					=       ''''''
      _A:  List[str]					=       []
      _A:  List[Any]					=       0
      _A:  Union[str, Any]					=       2_5_6
      _A:  List[str]					=       0
      _A:  Any					=       0
      _A:  List[str]					=       0
      _A:  str					=       0


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							List[str]     ):

      """simple docstring"""
      _A:  Optional[int]					=       cva.imread(lowerCAmelCase_			,			0     )
      _A:  Tuple					=       copy.deepcopy(self.img     )
      _A			,  _A			,  _A:  Dict					=       plt.hist(self.img.ravel()			,			2_5_6			,			[0, 2_5_6]			,			label='''x'''     )
      _A:  str					=       np.sum(lowerCAmelCase_     )
      for i in range(len(lowerCAmelCase_     )     ):
         _A:  int					=       x[i] / self.k
         self.sk += prk
         _A:  Optional[int]					=       (self.L - 1) * self.sk
         if self.rem != 0:
            _A:  List[str]					=       int(last % last     )
         _A:  Union[str, Any]					=       int(last + 1 if self.rem >= 0.5 else last     )
         self.last_list.append(lowerCAmelCase_     )
         _A:  Union[str, Any]					=       int(np.ma.count(self.img     ) / self.img[1].size     )
         _A:  Any					=       self.img[1].size
      for i in range(self.number_of_cols     ):
         for j in range(self.number_of_rows     ):
            _A:  List[str]					=       self.img[j][i]
            if num != self.last_list[num]:
               _A:  Optional[Any]					=       self.last_list[num]
      cva.imwrite('''output_data/output.jpg'''			,			self.img     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      plt.hist(self.img.ravel()			,			2_5_6			,			[0, 2_5_6]     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      cva.imshow('''Output-Image'''			,			self.img     )
      cva.imshow('''Input-Image'''			,			self.original_image     )
      cva.waitKey(5_0_0_0     )
      cva.destroyAllWindows()


if __name__ == "__main__":
   UpperCAmelCase__		:     List[Any]     			=      os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
   UpperCAmelCase__		:     Any     			=      ConstantStretch()
   stretcher.stretch(file_path)
   stretcher.plot_histogram()
   stretcher.show_image()





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   lowerCamelCase__							(					a      )      ->   int:
   _A:  int					=       filter(lambda a      : p.requires_grad		,		model.parameters()      )
   _A:  Dict					=       sum([np.prod(p.size()      ) for p in model_parameters]      )
   return params


UpperCAmelCase__		:     str     			=      logging.getLogger(__name__)






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if metric == "rouge2":
      _A:  Tuple					=       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A:  List[str]					=       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A:  str					=       '''{val_avg_em:.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.'''      )

   _A:  List[str]					=       ModelCheckpoint(
       dirpath=a		,		filename=a		,		monitor=f"""val_{metric}"""		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
   return checkpoint_callback






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   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  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       {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  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any=True     ):

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

      if not save_generations:
         return

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


   @rank_zero_only
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      try:
         _A:  Union[str, Any]					=       pl_module.model.model.num_parameters()
      except AttributeError:
         _A:  Any					=       pl_module.model.num_parameters()

      _A:  Optional[int]					=       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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule     ):

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


   @rank_zero_only
   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							Optional[Any]     ):

      """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")



