






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 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

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


__magic_name__   :  List[str]        =							logging.getLogger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """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}'
      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=_UpperCamelCase       ,							filename=_UpperCamelCase       ,							monitor=F'val_{metric}'       ,							mode='max'       ,							save_top_k=3       ,							every_n_epochs=1       ,							)
      return checkpoint_callback
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """simple docstring"""

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





class 					A__     (     pl.Callback ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  {f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
           pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE )







     @rank_zero_only
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    pl.Trainer   ,						_SCREAMING_SNAKE_CASE      :    pl.LightningModule   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=True ):






           """simple docstring"""




           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=_SCREAMING_SNAKE_CASE )
                 generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
           with open(_SCREAMING_SNAKE_CASE   ,						'a+' ) as writer:
                 for key in sorted(_SCREAMING_SNAKE_CASE ):
                       if key in ["log", "progress_bar", "preds"]:
                             continue
                       UpperCamelCase					       =  metrics[key]
                       if isinstance(_SCREAMING_SNAKE_CASE   ,						torch.Tensor ):
                             UpperCamelCase					       =  val.item()
                       UpperCamelCase					       =  f'{key}: {val:.6f}\n'
                       writer.write(_SCREAMING_SNAKE_CASE )

           if not save_generations:
                 return

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







     @rank_zero_only
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




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

           UpperCamelCase					       =  count_trainable_parameters(_SCREAMING_SNAKE_CASE )
           # 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   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    pl.Trainer   ,						_SCREAMING_SNAKE_CASE      :    pl.LightningModule ):






           """simple docstring"""




           save_json(pl_module.metrics   ,						pl_module.metrics_save_path )
           return self._write_logs(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						'test' )







     @rank_zero_only
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    pl.Trainer   ,						_SCREAMING_SNAKE_CASE      :    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")











def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count < 0:
            raise ValueError('The given input must be positive')

      # get the generated string sequence
      UpperCamelCase					       =  gray_code_sequence_string(_UpperCamelCase)
      #
      # convert them to integers
      for i in range(len(_UpperCamelCase)):
            UpperCamelCase					       =  int(sequence[i]       ,							2)

      return sequence
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count == 0:
            return ["0"]

      if bit_count == 1:
            return ["0", "1"]

      UpperCamelCase					       =  1 << bit_count  # defines the length of the sequence
      # 1<< n is equivalent to 2^n

      # recursive answer will generate answer for n-1 bits
      UpperCamelCase					       =  gray_code_sequence_string(bit_count - 1)

      UpperCamelCase					       =  []

      # append 0 to first half of the smaller sequence generated
      for i in range(seq_len // 2):
            UpperCamelCase					       =  '0' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      # append 1 to second half ... start from the end of the list
      for i in reversed(range(seq_len // 2)):
            UpperCamelCase					       =  '1' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()



