




'''simple docstring'''

from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					for param, grad_param in zip(model_a.parameters(), model_b.parameters()      ):
										if not param.requires_grad:
															continue
										if not did_step:
															# Grads should not be in sync
															assert (
															    torch.allclose(param.grad, grad_param.grad      ) is False
															), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
										else:
															# Grads should be in sync
															assert (
															    torch.allclose(param.grad, grad_param.grad      ) is True
															), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=True      )						->   List[Any]:

					'''simple docstring'''

					model.train()
					snake_case_		     =							model(__UpperCAmelCase      )
					snake_case_		     =							F.mse_loss(__UpperCAmelCase, target.to(output.device      )      )
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase=False      )						->   Tuple:

					'''simple docstring'''

					set_seed(42      )
					snake_case_		     =							RegressionModel()
					snake_case_		     =							deepcopy(__UpperCAmelCase      )
					snake_case_		     =							RegressionDataset(length=80      )
					snake_case_		     =							DataLoader(__UpperCAmelCase, batch_size=16      )
					model.to(accelerator.device      )
					if sched:
										snake_case_		     =							AdamW(params=model.parameters(), lr=1e-3      )
										snake_case_		     =							AdamW(params=ddp_model.parameters(), lr=1e-3      )
										snake_case_		     =							LambdaLR(__UpperCAmelCase, lr_lambda=lambda __UpperCAmelCase      : epoch**0.6_5      )
										snake_case_		     =							LambdaLR(__UpperCAmelCase, lr_lambda=lambda __UpperCAmelCase      : epoch**0.6_5      )
					# Make a copy of `model`
					if sched:
										snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							accelerator.prepare(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
					else:
										snake_case_     ,snake_case_		     =							accelerator.prepare(__UpperCAmelCase, __UpperCAmelCase      )
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader


def 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_     ,snake_case_     ,snake_case_		     =							get_training_setup(__UpperCAmelCase      )
					# Use a single batch
					snake_case_     ,snake_case_		     =							next(iter(__UpperCAmelCase      )      ).values()
					for iteration in range(3      ):
										# Gather the distributed inputs and targs for the base model
										snake_case_     ,snake_case_		     =							accelerator.gather((ddp_input, ddp_target)      )
										snake_case_     ,snake_case_		     =							input.to(accelerator.device      ), target.to(accelerator.device      )
										# Perform our initial ground truth step in non "DDP"
										step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(__UpperCAmelCase      ):
																				step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										else:
															# Sync grads
															step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										for param, ddp_param in zip(model.parameters(), ddp_model.parameters()      ):
															if not param.requires_grad:
																				continue
															assert torch.allclose(
															    param.grad, ddp_param.grad      ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration      )
										snake_case_		     =							ddp_input[torch.randperm(len(__UpperCAmelCase      )      )]


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_     ,snake_case_     ,snake_case_		     =							get_training_setup(__UpperCAmelCase      )
					# Use a single batch
					snake_case_     ,snake_case_		     =							next(iter(__UpperCAmelCase      )      ).values()
					for iteration in range(3      ):
										# Gather the distributed inputs and targs for the base model
										snake_case_     ,snake_case_		     =							accelerator.gather((ddp_input, ddp_target)      )
										snake_case_     ,snake_case_		     =							input.to(accelerator.device      ), target.to(accelerator.device      )
										# Perform our initial ground truth step in non "DDP"
										step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(__UpperCAmelCase      ):
																				step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										else:
															# Sync grads
															step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters(), ddp_model.parameters()      ):
															if not param.requires_grad:
																				continue
															if iteration % 2 == 0:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad, ddp_param.grad      ) is False
																				), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
															else:
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad, ddp_param.grad      ) is True
																				), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration      )
										snake_case_		     =							ddp_input[torch.randperm(len(__UpperCAmelCase      )      )]


def 						__magic_name__       (      __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							Accelerator(
					    split_batches=__UpperCAmelCase, dispatch_batches=__UpperCAmelCase, gradient_accumulation_steps=2      )
					# Test that context manager behaves properly
					snake_case_     ,snake_case_     ,snake_case_		     =							get_training_setup(__UpperCAmelCase      )
					for iteration, batch in enumerate(__UpperCAmelCase      ):
										snake_case_     ,snake_case_		     =							batch.values()
										# Gather the distributed inputs and targs for the base model
										snake_case_     ,snake_case_		     =							accelerator.gather((ddp_input, ddp_target)      )
										snake_case_     ,snake_case_		     =							input.to(accelerator.device      ), target.to(accelerator.device      )
										# Perform our initial ground truth step in non "DDP"
										step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(__UpperCAmelCase      ):
															step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters(), ddp_model.parameters()      ):
															if not param.requires_grad:
																				continue
															if ((iteration + 1) % 2 == 0) or (iteration == len(__UpperCAmelCase      ) - 1):
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad, ddp_param.grad      ) is True
																				), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
															else:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad, ddp_param.grad      ) is False
																				), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration      )
										snake_case_		     =							ddp_input[torch.randperm(len(__UpperCAmelCase      )      )]
					GradientState._reset_state()


def 						__magic_name__       (      __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							Accelerator(
					    split_batches=__UpperCAmelCase, dispatch_batches=__UpperCAmelCase, gradient_accumulation_steps=2      )
					# Test that context manager behaves properly
					snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							get_training_setup(__UpperCAmelCase, __UpperCAmelCase      )
					for iteration, batch in enumerate(__UpperCAmelCase      ):
										snake_case_     ,snake_case_		     =							batch.values()
										# Gather the distributed inputs and targs for the base model
										snake_case_     ,snake_case_		     =							accelerator.gather((ddp_input, ddp_target)      )
										snake_case_     ,snake_case_		     =							input.to(accelerator.device      ), target.to(accelerator.device      )
										# Perform our initial ground truth step in non "DDP"
										model.train()
										ddp_model.train()
										step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__UpperCAmelCase      )):
															if split_batches:
																				sched.step()
															else:
																				for _ in range(accelerator.num_processes      ):
																									sched.step()
										opt.zero_grad()
										# Perform gradient accumulation under wrapper
										with accelerator.accumulate(__UpperCAmelCase      ):
															step_model(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
															ddp_opt.step()
															ddp_sched.step()
															ddp_opt.zero_grad()

										# Learning rates should be the same
										assert (
										    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
										), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
										snake_case_		     =							(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__UpperCAmelCase      ))
										if accelerator.num_processes > 1:
															check_model_parameters(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration      )
					GradientState._reset_state()


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							Accelerator()

					snake_case_		     =							RegressionDataset(length=80      )
					snake_case_		     =							DataLoader(__UpperCAmelCase, batch_size=16      )
					snake_case_		     =							RegressionDataset(length=96      )
					snake_case_		     =							DataLoader(__UpperCAmelCase, batch_size=16      )
					snake_case_     ,snake_case_		     =							accelerator.prepare(__UpperCAmelCase, __UpperCAmelCase      )
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(__UpperCAmelCase      ):
										assert id(accelerator.gradient_state.active_dataloader      ) == id(__UpperCAmelCase      )
										if iteration < len(__UpperCAmelCase      ) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(__UpperCAmelCase      ):
																									assert id(accelerator.gradient_state.active_dataloader      ) == id(__UpperCAmelCase      )
																									if batch_num < len(__UpperCAmelCase      ) - 1:
																														assert not accelerator.gradient_state.end_of_dataloader
																									else:
																														assert accelerator.gradient_state.end_of_dataloader
										else:
															assert accelerator.gradient_state.end_of_dataloader
					assert accelerator.gradient_state.active_dataloader is None


def 						__magic_name__       (      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							Accelerator()
					snake_case_		     =							accelerator.state
					if state.local_process_index == 0:
										print('''**Test `accumulate` gradient accumulation with dataloader break**'''      )
					test_dataloader_break()
					if state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print('''**Test NOOP `no_sync` context manager**'''      )
										test_noop_sync(__UpperCAmelCase      )
					if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
										if state.local_process_index == 0:
															print('''**Test Distributed `no_sync` context manager**'''      )
										test_distributed_sync(__UpperCAmelCase      )
					if state.distributed_type == DistributedType.MULTI_GPU:
										for split_batch in [True, False]:
															for dispatch_batches in [True, False]:
																				if state.local_process_index == 0:
																									print(
																									    '''**Test `accumulate` gradient accumulation, ''', F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**", )
																				test_gradient_accumulation(__UpperCAmelCase, __UpperCAmelCase      )

    # Currently will break on torch 2.0 +, need to investigate why
					if is_torch_version('''<''', '''2.0'''      ) or state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print(
															    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''', '''`split_batches=False`, `dispatch_batches=False`**''', )
										test_gradient_accumulation_with_opt_and_scheduler()
										if state.distributed_type == DistributedType.MULTI_GPU:
															for split_batch in [True, False]:
																				for dispatch_batches in [True, False]:
																									if not split_batch and not dispatch_batches:
																														continue
																									if state.local_process_index == 0:
																														print(
																														    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''', F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**", )
																									test_gradient_accumulation_with_opt_and_scheduler(__UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					main()


if __name__ == "__main__":
							main()







'''simple docstring'''

from collections.abc import Sequence
from queue import Queue






class 		a							:


				def __init__(						self       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int=None			,							lowercase_       :   List[Any]=None   ):
									snake_case_		     =							start
									snake_case_		     =							end
									snake_case_		     =							val
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							left
									snake_case_		     =							right


				def __repr__(						self       :   Any   ):
									return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   Sequence			,							lowercase_       :   List[str]   ):
									snake_case_		     =							collection
									snake_case_		     =							function
									if self.collection:
														snake_case_		     =							self._build_tree(0			,							len(lowercase_   ) - 1   )


				def     A_					(						self       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									self._update_tree(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]   ):
									return self._query_range(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   int			,							lowercase_       :   int   ):
									if start == end:
														return SegmentTreeNode(lowercase_			,							lowercase_			,							self.collection[start]   )
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							self._build_tree(lowercase_			,							lowercase_   )
									snake_case_		     =							self._build_tree(mid + 1			,							lowercase_   )
									return SegmentTreeNode(lowercase_			,							lowercase_			,							self.fn(left.val			,							right.val   )			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									if node.start == i and node.end == i:
														snake_case_		     =							val
														return
									if i <= node.mid:
														self._update_tree(node.left			,							lowercase_			,							lowercase_   )
									else:
														self._update_tree(node.right			,							lowercase_			,							lowercase_   )
									snake_case_		     =							self.fn(node.left.val			,							node.right.val   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									if node.start == i and node.end == j:
														return node.val

									if i <= node.mid:
														if j <= node.mid:
																			# range in left child tree
																			return self._query_range(node.left			,							lowercase_			,							lowercase_   )
														else:
																			# range in left child tree and right child tree
																			return self.fn(
																			    self._query_range(node.left			,							lowercase_			,							node.mid   )			,							self._query_range(node.right			,							node.mid + 1			,							lowercase_   )			,							)
									else:
														# range in right child tree
														return self._query_range(node.right			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any   ):
									if self.root is not None:
														snake_case_		     =							Queue()
														queue.put(self.root   )
														while not queue.empty():
																			snake_case_		     =							queue.get()
																			yield node

																			if node.left is not None:
																								queue.put(node.left   )

																			if node.right is not None:
																								queue.put(node.right   )


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('*' * 50)
														a    :							List[Any]										=    SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()

