






"""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 import save_json







def 					lowerCamelCase__	(							_lowerCamelCase	:		int			)				->    Any:
  lowerCamelCase_   					=						filter(lambda _lowerCamelCase			: p.requires_grad		,    model.parameters()			)
  lowerCamelCase_   					=						sum([np.prod(p.size()			) for p in model_parameters]			)
  return params


_SCREAMING_SNAKE_CASE :    str					   =						logging.getLogger(__name__)






class 					a						( pl.Callback		):






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

      if not save_generations:
        return

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






    @rank_zero_only
    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any] )					->		int:
      try:
        lowerCamelCase_   					=						pl_module.model.model.num_parameters()
      except AttributeError:
        lowerCamelCase_   					=						pl_module.model.num_parameters()

      lowerCamelCase_   					=						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        UpperCamelCase	(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							pl.Trainer	,						__SCREAMING_SNAKE_CASE					:							pl.LightningModule )					->		str:
      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        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							pl.Trainer	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Optional[int]:
      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__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Any=1		,    _lowerCamelCase	:		Any=False			)				->    Any:
  if metric == "rouge2":
    lowerCamelCase_   					=						'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    lowerCamelCase_   					=						'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "loss":
    lowerCamelCase_   					=						'{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.'			)

  lowerCamelCase_   					=						ModelCheckpoint(
      dirpath=_lowerCamelCase		,    filename=_lowerCamelCase		,    monitor=F'''val_{metric}'''		,    mode='min' if 'loss' in metric else 'max'		,    save_top_k=_lowerCamelCase		,    )
  return checkpoint_callback







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Dict			)				->    Optional[Any]:
  return EarlyStopping(
      monitor=F'''val_{metric}'''		,    mode='min' if 'loss' in metric else 'max'		,    patience=_lowerCamelCase		,    verbose=_lowerCamelCase		,    )












"""simple docstring"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




