

"""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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=True				):







							'''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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=False				):







							'''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**0.65				)
														snake_case_          =   LambdaLR(UpperCamelCase__       ,							lr_lambda=lambda UpperCamelCase__				: epoch**0.65				)
							# 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  __lowerCamelCase     (			UpperCamelCase__				):







							'''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  __lowerCamelCase     (			UpperCamelCase__				):







							'''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  __lowerCamelCase     (			UpperCamelCase__=False       ,							UpperCamelCase__=False				):







							'''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  __lowerCamelCase     (			UpperCamelCase__=False       ,							UpperCamelCase__=False				):







							'''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  __lowerCamelCase     (			):







							'''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  __lowerCamelCase     (			):







							'''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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							create_all_state(1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							[]       ,							UpperCamelCase__				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):







							'''simple docstring'''






							if level == 0:
														total_list.append(current_list[:]				)
														return

							for i in range(UpperCamelCase__       ,							total_number - level + 2				):
														current_list.append(UpperCamelCase__				)
														create_all_state(i + 1       ,							UpperCamelCase__       ,							level - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														current_list.pop()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for i in total_list:
														print(*UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	4
					_UpperCAmelCase		:       Tuple						    =	2
					_UpperCAmelCase		:       Optional[int]						    =	generate_all_combinations(n, k)
					print_all_state(total_list)




