'''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 			UpperCamelCase__       (       _lowercase     :					List[Any]  )				->						str:
  __UpperCAmelCase:       int		=			filter(lambda _lowercase  : p.requires_grad ,     model.parameters()  )
  __UpperCAmelCase:       Union[str, Any]		=			sum([np.prod(p.size()  ) for p in model_parameters]  )
  return params


SCREAMING_SNAKE_CASE_						 =      logging.getLogger(__name__)






def 			UpperCamelCase__       (       _lowercase     :					str ,     _lowercase     :					Any  )				->						List[str]:
  if metric == "rouge2":
    __UpperCAmelCase:       Dict		=			'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    __UpperCAmelCase:       Dict		=			'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "em":
    __UpperCAmelCase:       List[str]		=			'{val_avg_em:.4f}-{step_count}'
  elif metric == "loss":
    __UpperCAmelCase:       int		=			'{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:       int		=			ModelCheckpoint(
      dirpath=lowerCAmelCase__ ,     filename=lowerCAmelCase__ ,     monitor=F'''val_{metric}''' ,     mode="""max""" ,     save_top_k=1 ,     every_n_epochs=1 ,     )

  return checkpoint_callback






def 			UpperCamelCase__       (       _lowercase     :					List[Any] ,     _lowercase     :					Dict  )				->						str:
  return EarlyStopping(
      monitor=F'''val_{metric}''' ,     mode="""min""" if """loss""" in metric else """max""" ,     patience=lowerCAmelCase__ ,     verbose=lowerCAmelCase__ ,     )







class  a			(							pl.Callback					):



  """simple docstring"""







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       Optional[int]		=			{F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
    pl_module.logger.log_metrics(snake_case_       )







  @rank_zero_only
  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_=True       ):

    '''simple docstring'''







    logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
    __UpperCAmelCase:       Optional[int]		=			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:       Union[str, Any]		=			Path(pl_module.hparams.output_dir       )
    if type_path == "test":
      __UpperCAmelCase:       int		=			od / 'test_results.txt'
      __UpperCAmelCase:       str		=			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:       List[str]		=			od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
      __UpperCAmelCase:       Union[str, Any]		=			od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
      results_file.parent.mkdir(exist_ok=snake_case_       )
      generations_file.parent.mkdir(exist_ok=snake_case_       )
    with open(snake_case_ ,     """a+"""       ) as writer:
      for key in sorted(snake_case_       ):
        if key in ["log", "progress_bar", "preds"]:
          continue
        __UpperCAmelCase:       List[str]		=			metrics[key]
        if isinstance(snake_case_ ,     torch.Tensor       ):
          __UpperCAmelCase:       Union[str, Any]		=			val.item()
        __UpperCAmelCase:       str		=			F'''{key}: {val:.6f}\n'''
        writer.write(snake_case_       )

    if not save_generations:
      return

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







  @rank_zero_only
  def  lowercase_	(				self ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    try:
      __UpperCAmelCase:       Dict		=			pl_module.model.model.num_parameters()
    except AttributeError:
      __UpperCAmelCase:       List[Any]		=			pl_module.model.num_parameters()

    __UpperCAmelCase:       Dict		=			count_trainable_parameters(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  lowercase_	(				self ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    save_json(pl_module.metrics ,     pl_module.metrics_save_path       )
    return self._write_logs(snake_case_ ,     snake_case_ ,     """test"""       )







  @rank_zero_only
  def  lowercase_	(				self ,     snake_case_ ,     snake_case_       ):

    '''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")






_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


if __name__ == "__main__":
		print(F'''{solution() = }''')
