

'''simple docstring'''
from __future__ import annotations

import math
class 					_snake_case		:






			def __init__(  self			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  size
				# approximate the overall size of segment tree with given value
				UpperCAmelCase_     :      str						=  [0 for i in range(0			,4 * size     )]
				# create array to store lazy update
				UpperCAmelCase_     :      int						=  [0 for i in range(0			,4 * size     )]
				UpperCAmelCase_     :      Union[str, Any]						=  [0 for i in range(0			,4 * size     )]  # flag for lazy update






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return idx * 2






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return idx * 2 + 1






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				if left_element == right_element:
					UpperCAmelCase_     :      Optional[Any]						=  a[left_element - 1]
				else:
					UpperCAmelCase_     :      int						=  (left_element + right_element) // 2
					self.build(self.left(_snake_case     )			,_snake_case			,_snake_case			,_snake_case     )
					self.build(self.right(_snake_case     )			,mid + 1			,_snake_case			,_snake_case     )
					UpperCAmelCase_     :      int						=  max(
					    self.segment_tree[self.left(_snake_case     )]			,self.segment_tree[self.right(_snake_case     )]     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				if self.flag[idx] is True:
					UpperCAmelCase_     :      Any						=  self.lazy[idx]
					UpperCAmelCase_     :      Any						=  False
					if left_element != right_element:
						UpperCAmelCase_     :      int						=  self.lazy[idx]
						UpperCAmelCase_     :      Dict						=  self.lazy[idx]
						UpperCAmelCase_     :      int						=  True
						UpperCAmelCase_     :      Tuple						=  True

				if right_element < a or left_element > b:
					return True
				if left_element >= a and right_element <= b:
					UpperCAmelCase_     :      int						=  val
					if left_element != right_element:
						UpperCAmelCase_     :      Dict						=  val
						UpperCAmelCase_     :      Optional[Any]						=  val
						UpperCAmelCase_     :      int						=  True
						UpperCAmelCase_     :      List[Any]						=  True
					return True
				UpperCAmelCase_     :      Any						=  (left_element + right_element) // 2
				self.update(self.left(_snake_case     )			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     )
				self.update(self.right(_snake_case     )			,mid + 1			,_snake_case			,_snake_case			,_snake_case			,_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  max(
				    self.segment_tree[self.left(_snake_case     )]			,self.segment_tree[self.right(_snake_case     )]     )
				return True






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				if self.flag[idx] is True:
					UpperCAmelCase_     :      Optional[int]						=  self.lazy[idx]
					UpperCAmelCase_     :      Dict						=  False
					if left_element != right_element:
						UpperCAmelCase_     :      Dict						=  self.lazy[idx]
						UpperCAmelCase_     :      Dict						=  self.lazy[idx]
						UpperCAmelCase_     :      List[str]						=  True
						UpperCAmelCase_     :      List[Any]						=  True
				if right_element < a or left_element > b:
					return -math.inf
				if left_element >= a and right_element <= b:
					return self.segment_tree[idx]
				UpperCAmelCase_     :      List[Any]						=  (left_element + right_element) // 2
				UpperCAmelCase_     :      List[str]						=  self.query(self.left(_snake_case     )			,_snake_case			,_snake_case			,_snake_case			,_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.query(self.right(_snake_case     )			,mid + 1			,_snake_case			,_snake_case			,_snake_case     )
				return max(_snake_case			,_snake_case     )






			def __str__(  self     ):
				return str([self.query(1			,1			,self.size			,_snake_case			,_snake_case     ) for i in range(1			,self.size + 1     )]     )


if __name__ == "__main__":
					_lowerCamelCase      					=     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
					_lowerCamelCase      					=     15
					_lowerCamelCase      					=     SegmentTree(size)
					segt.build(1, 1, size, A)
					print(segt.query(1, 1, size, 4, 6))
					print(segt.query(1, 1, size, 7, 11))
					print(segt.query(1, 1, size, 7, 12))
					segt.update(1, 1, size, 1, 3, 111)
					print(segt.query(1, 1, size, 1, 15))
					segt.update(1, 1, size, 7, 8, 235)
					print(segt)






"""simple docstring"""


import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_lowercase				 =					get_logger(__name__)






def    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  List[Any]						,   snake_case__ :  int						,   snake_case__ :  int						,   snake_case__ :  str=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = model.state_dict()
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			if accelerator.process_index == 0:
				logger.info(F'Saving model to {output_model_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Saving model to {output_model_file}'  )
			torch.save(snake_case__						,   snake_case__  )
			logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving model to {ckpt_dir}'  )
			A     = {'model': state_dict}

			dist_cp.save_state_dict(
			    state_dict=snake_case__						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Model saved to {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  List[str]						,   snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  Any=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if type(snake_case__  ) != FSDP and accelerator.process_index != 0:
				if not fsdp_plugin.sync_module_states:
					raise ValueError(
					    'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
					    'initializing FSDP object'  )
				return
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = (
			    os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			    if F'{MODEL_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading model from {ckpt_dir}'  )
			A     = {'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   planner=DefaultLoadPlanner()						,   )
			A     = state_dict['model']
			logger.info(F'Model loaded from {ckpt_dir}'  )
		model.load_state_dict(snake_case__  )






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Optional[int]						,   snake_case__ :  Optional[int]						,   snake_case__ :  Dict						,   snake_case__ :  List[str]						,   snake_case__ :  Any=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = FSDP.optim_state_dict(snake_case__						,   snake_case__  )
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				A     = (
				    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
				)
				A     = os.path.join(snake_case__						,   snake_case__  )
				logger.info(F'Saving Optimizer state to {output_optimizer_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Optimizer state saved in {output_optimizer_file}'  )
		else:
			A     = os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving Optimizer state to {ckpt_dir}'  )
			dist_cp.save_state_dict(
			    state_dict={'optimizer': optim_state}						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Optimizer state saved in {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[str]						,   snake_case__ :  List[str]						,   snake_case__ :  int						,   snake_case__ :  Optional[int]=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = None
			# below check should work but currently it isn't working (mostly opytorch issue),
			# in the meantime disabling it at the cost of excess memory usage
			# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
			A     = (
			    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading Optimizer state from {input_optimizer_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Optimizer state loaded from {input_optimizer_file}'  )
		else:
			A     = (
			    os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			    if F'{OPTIMIZER_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading Optimizer from {ckpt_dir}'  )
			A     = load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()						,   optimizer_key='optimizer'						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   )
			A     = optim_state['optimizer']
			logger.info(F'Optimizer loaded from {ckpt_dir}'  )
		A     = FSDP.optim_state_dict_to_load(snake_case__						,   snake_case__						,   snake_case__  )
		optimizer.load_state_dict(snake_case__  )