




'''simple docstring'''

from __future__ import annotations

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





class __magic_name__(						__lowerCAmelCase      ):






						@abstractmethod
						def __lt__(   self						:						T					,   __UpperCamelCase						:						T       ):



									'''simple docstring'''






									pass






						@abstractmethod
						def __gt__(   self						:						T					,   __UpperCamelCase						:						T       ):



									'''simple docstring'''






									pass







						@abstractmethod
						def __eq__(   self						:						T					,   __UpperCamelCase						:						object       ):



									'''simple docstring'''






									pass







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





class __magic_name__(						Generic[T]      ):






						def __init__(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						[]
									snake_case__          =						0






						def __repr__(   self						:						List[Any]       ):



									'''simple docstring'''






									return str(self.h       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						2 * parent_idx + 1
									if left_child_index < self.heap_size:
												return left_child_index
									return None






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						2 * parent_idx + 2
									if right_child_index < self.heap_size:
												return right_child_index
									return None






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						Iterable[T]       ):



									'''simple docstring'''






									snake_case__          =						list(__UpperCamelCase       )
									snake_case__          =						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(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									if self.heap_size >= 2:
												snake_case__          =						self.h[0]
												snake_case__          =						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						:						Union[str, Any]					,   __UpperCamelCase						:						T       ):



									'''simple docstring'''






									self.h.append(__UpperCamelCase       )
									snake_case__          =						(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
												self.max_heapify(__UpperCamelCase       )
												snake_case__          =						(idx - 1) // 2







						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						self.heap_size
									for j in range(size - 1					,   0					,   -1       ):
												snake_case__    ,			snake_case__          =						self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0       )
									snake_case__          =						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}")

				a__         =     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,      )



