




"""simple docstring"""





class       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     Any      ):
										snake_case__       :					int										=					data
										snake_case__       :					BinaryTreeNode | None										=					None
										snake_case__       :					BinaryTreeNode | None										=					None







def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						if node is None:
												snake_case__       :					Optional[Any]										=					BinaryTreeNode(UpperCamelCase__		)
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												snake_case__       :					str										=					insert(node.left_child      ,			UpperCamelCase__		)
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												snake_case__       :					Tuple										=					insert(node.right_child      ,			UpperCamelCase__		)
						return node

def 		lowerCAmelCase							( lowerCamelCase		):  # if node is None,return
						'''simple docstring'''


						if node:
												snake_case__       :					Any										=					inorder(node.left_child		)
												snake_case__       :					int										=					[*inorder_array, node.data]
												snake_case__       :					Optional[int]										=					inorder_array + inorder(node.right_child		)
						else:
												snake_case__       :					List[str]										=					[]
						return inorder_array

def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					Any										=					insert(UpperCamelCase__      ,			15		)
						insert(UpperCamelCase__      ,			10		)
						insert(UpperCamelCase__      ,			25		)
						insert(UpperCamelCase__      ,			6		)
						insert(UpperCamelCase__      ,			14		)
						insert(UpperCamelCase__      ,			20		)
						insert(UpperCamelCase__      ,			60		)
						return root

def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					Dict										=					make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''		)
						inorder(UpperCamelCase__		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()








"""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


_lowercase	      =    get_logger(__name__)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = None      ,			):
						'''simple docstring'''


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

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=None      ,			lowerCamelCase=None      ,			**lowerCamelCase      ,			):
						'''simple docstring'''


						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:
												snake_case__       :					str										=					'''cpu'''
						elif map_location == "on_device":
												snake_case__       :					List[Any]										=					PartialState().device
						# Model states
						for i, model in enumerate(lowerCamelCase		):
												snake_case__       :					Optional[int]										=					f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
												snake_case__       :					int										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												models[i].load_state_dict(torch.load(lowerCamelCase      ,			map_location=lowerCamelCase		)      ,			**lowerCamelCase		)
						logger.info('''All model weights loaded successfully'''		)

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

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

						# GradScaler state
						if scaler is not None:
												snake_case__       :					Optional[Any]										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												scaler.load_state_dict(torch.load(lowerCamelCase		)		)
												logger.info('''GradScaler state loaded successfully'''		)

						# Random states
						try:
												snake_case__       :					Optional[int]										=					torch.load(os.path.join(lowerCamelCase      ,			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							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = 0		):
						'''simple docstring'''


						snake_case__       :					Union[str, Any]										=					Path(lowerCamelCase		) / f'''custom_checkpoint_{index}.pkl'''
						logger.info(f'''Saving the state of {get_pretty_name(lowerCamelCase		)} to {save_location}'''		)
						torch.save(obj.state_dict()      ,			lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = 0		):
						'''simple docstring'''


						snake_case__       :					Any										=					f'''{path}/custom_checkpoint_{index}.pkl'''
						logger.info(f'''Loading the state of {get_pretty_name(lowerCamelCase		)} from {load_location}'''		)
						obj.load_state_dict(torch.load(lowerCamelCase      ,			map_location='''cpu'''		)		)


