






"""simple docstring"""



from __future__ import annotations

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






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):


							@abstractmethod
							def __lt__(      self   :    T      ,     snake_case   :    T				):
													'''simple docstring'''





													pass


							@abstractmethod
							def __gt__(      self   :    T      ,     snake_case   :    T				):
													'''simple docstring'''





													pass


							@abstractmethod
							def __eq__(      self   :    T      ,     snake_case   :    object				):
													'''simple docstring'''





													pass




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






class 				__SCREAMING_SNAKE_CASE    (					Generic[T]		):


							def __init__(      self   :    List[str]				):
													'''simple docstring'''





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


							def __repr__(      self   :    Optional[int]				):
													'''simple docstring'''





													return str(self.h				)


							def     _UpperCamelCase							(      self   :    int      ,     snake_case   :    int				):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    List[str]      ,     snake_case   :    int				):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    Union[str, Any]      ,     snake_case   :    int				):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    List[Any]      ,     snake_case   :    int				):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    int      ,     snake_case   :    Iterable[T]				):
													'''simple docstring'''





													A__    :	str			=	list(snake_case				)
													A__    :	Optional[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(snake_case				)


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													if self.heap_size >= 2:
																			A__    :	List[str]			=	self.h[0]
																			A__    :	List[str]			=	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     _UpperCamelCase							(      self   :    Optional[int]      ,     snake_case   :    T				):
													'''simple docstring'''





													self.h.append(snake_case				)
													A__    :	Dict			=	(self.heap_size - 1) // 2
													self.heap_size += 1
													while idx >= 0:
																			self.max_heapify(snake_case				)
																			A__    :	Any			=	(idx - 1) // 2


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__    :	str			=	self.heap_size
													for j in range(size - 1      ,     0      ,     -1				):
																			A__	,	A__    :	int			=	self.h[j], self.h[0]
																			self.heap_size -= 1
																			self.max_heapify(0				)
													A__    :	Optional[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}')

												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')














"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       List[dict],    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       GradScaler = None,    )		->Any:
						# Model states
						for i, state in enumerate(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	int			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Model weights saved in {output_model_file}'   )
						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	str			=	opt.state_dict()
												A__    :	Any			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Optional[int]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Optimizer state saved in {output_optimizer_file}'   )
						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	scheduler.state_dict()
												A__    :	Union[str, Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Tuple			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Scheduler state saved in {output_scheduler_file}'   )
						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	scaler.state_dict()
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'   )
						# Random number generator states
						A__    :	str			=	{}
						A__    :	str			=	f'{RNG_STATE_NAME}_{process_index}.pkl'
						A__    :	Optional[Any]			=	random.getstate()
						A__    :	str			=	np.random.get_state()
						A__    :	List[str]			=	torch.get_rng_state()
						if is_xpu_available():
												A__    :	Optional[Any]			=	torch.xpu.get_rng_state_all()
						else:
												A__    :	str			=	torch.cuda.get_rng_state_all()
						if is_tpu_available():
												A__    :	Tuple			=	xm.get_rng_state()
						A__    :	str			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
						torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
						logger.info(f'Random states saved in {output_states_file}'   )
						return output_dir




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]=None,    UpperCAmelCase__							:       str=None,    **UpperCAmelCase__							:       Optional[int],    )		->List[str]:
						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""   )
						if map_location is None:
												A__    :	Union[str, Any]			=	"""cpu"""
						elif map_location == "on_device":
												A__    :	Any			=	PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase__   ):
												A__    :	Any			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												models[i].load_state_dict(torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   ),    **UpperCAmelCase__   )
						logger.info("""All model weights loaded successfully"""   )

						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Dict			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												A__    :	List[Any]			=	torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   )
												optimizers[i].load_state_dict(UpperCAmelCase__   )
						logger.info("""All optimizer states loaded successfully"""   )

						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	List[Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scheduler.load_state_dict(torch.load(UpperCAmelCase__   )   )
						logger.info("""All scheduler states loaded successfully"""   )

						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scaler.load_state_dict(torch.load(UpperCAmelCase__   )   )
												logger.info("""GradScaler state loaded successfully"""   )

						# Random states
						try:
												A__    :	Any			=	torch.load(os.path.join(UpperCAmelCase__,    f'{RNG_STATE_NAME}_{process_index}.pkl'   )   )
												random.setstate(states["""random_state"""]   )
												np.random.set_state(states["""numpy_random_seed"""]   )
												torch.set_rng_state(states["""torch_manual_seed"""]   )
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]   )
												else:
																		torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]   )
												if is_tpu_available():
																		xm.set_rng_state(states["""xm_seed"""]   )
												logger.info("""All random states loaded successfully"""   )
						except Exception:
												logger.info("""Could not load random states"""   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       int = 0   )		->Dict:
						A__    :	Dict			=	Path(UpperCAmelCase__   ) / f'custom_checkpoint_{index}.pkl'
						logger.info(f'Saving the state of {get_pretty_name(UpperCAmelCase__   )} to {save_location}'   )
						torch.save(obj.state_dict(),    UpperCAmelCase__   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int = 0   )		->str:
						A__    :	Optional[int]			=	f'{path}/custom_checkpoint_{index}.pkl'
						logger.info(f'Loading the state of {get_pretty_name(UpperCAmelCase__   )} from {load_location}'   )
						obj.load_state_dict(torch.load(UpperCAmelCase__,    map_location="""cpu"""   )   )






