"""simple docstring"""
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class   __snake_case			(	__lowerCAmelCase							):



      @abstractmethod
      def __lt__(		self       ,     lowercase)      ->     bool:





        '''simple docstring'''





        pass



      @abstractmethod
      def __gt__(		self       ,     lowercase)      ->     bool:





        '''simple docstring'''





        pass



      @abstractmethod
      def __eq__(		self       ,     lowercase)      ->     bool:





        '''simple docstring'''





        pass







lowercase__   			=      TypeVar('T', bound=Comparable)




class   __snake_case			(	Generic[T]							):



      def __init__(		self)      ->     None:





        '''simple docstring'''





        a__:	list[T]     				=      []
        a__:	int     				=      0



      def __repr__(		self)      ->     str:





        '''simple docstring'''





        return str(self.h)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int | None:





        '''simple docstring'''





        if child_idx > 0:
          return (child_idx - 1) // 2
        return None



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int | None:





        '''simple docstring'''





        a__:	List[Any]     				=      2 * parent_idx + 1
        if left_child_index < self.heap_size:
          return left_child_index
        return None



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int | None:





        '''simple docstring'''





        a__:	List[str]     				=      2 * parent_idx + 2
        if right_child_index < self.heap_size:
          return right_child_index
        return None



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     None:





        '''simple docstring'''





        if index < self.heap_size:
          a__:	int     				=      index
          a__:	int     				=      self.left_child_idx(lowercase)
          a__:	Dict     				=      self.right_child_idx(lowercase)
          # check which child is larger than its parent
          if left_child is not None and self.h[left_child] > self.h[violation]:
            a__:	List[Any]     				=      left_child
          if right_child is not None and self.h[right_child] > self.h[violation]:
            a__:	List[str]     				=      right_child
          # if violation indeed exists
          if violation != index:
            # swap to fix the violation
            a__				,       a__:	int     				=      self.h[index], self.h[violation]
            # fix the subsequent violation recursively if any
            self.max_heapify(lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     None:





        '''simple docstring'''





        a__:	List[Any]     				=      list(lowercase)
        a__:	int     				=      len(self.h)
        if self.heap_size > 1:
          # max_heapify from right to left but exclude leaves (last level)
          for i in range(self.heap_size // 2 - 1       ,     -1       ,     -1):
            self.max_heapify(lowercase)



      def     lowerCamelCase_  (		self)      ->     T:





        '''simple docstring'''





        if self.heap_size >= 2:
          a__:	Any     				=      self.h[0]
          a__:	int     				=      self.h.pop(-1)
          self.heap_size -= 1
          self.max_heapify(0)
          return me
        elif self.heap_size == 1:
          self.heap_size -= 1
          return self.h.pop(-1)
        else:
          raise Exception('Empty heap')



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     None:





        '''simple docstring'''





        self.h.append(lowercase)
        a__:	str     				=      (self.heap_size - 1) // 2
        self.heap_size += 1
        while idx >= 0:
          self.max_heapify(lowercase)
          a__:	Tuple     				=      (idx - 1) // 2



      def     lowerCamelCase_  (		self)      ->     None:





        '''simple docstring'''





        a__:	Any     				=      self.heap_size
        for j in range(size - 1       ,     0       ,     -1):
          a__				,       a__:	str     				=      self.h[j], self.h[0]
          self.heap_size -= 1
          self.max_heapify(0)
        a__:	Union[str, Any]     				=      size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
     [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
     [-45, -2, -5],
 ]:
  print(f"unsorted array: {unsorted}")

  lowercase__   			=      Heap()
  heap.build_max_heap(unsorted)
  print(f"after build heap: {heap}")

  print(f"max value: {heap.extract_max()}")
  print(f"after max value removed: {heap}")

  heap.insert(100)
  print(f"after new value 100 inserted: {heap}")

  heap.heap_sort()
  print(f"heap-sorted array: {heap}\n")








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    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {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        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :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
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :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.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    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
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    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        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         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    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{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." )

  lowercase     :int   						=    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    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



