


"""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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=True       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					set_seed(42       )
					__SCREAMING_SNAKE_CASE						       =    RegressionModel()
					__SCREAMING_SNAKE_CASE						       =    deepcopy(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    RegressionDataset(length=80       )
					__SCREAMING_SNAKE_CASE						       =    DataLoader(lowerCAmelCase_  ,						batch_size=16       )
					model.to(accelerator.device       )
					if sched:
										__SCREAMING_SNAKE_CASE						       =    AdamW(params=model.parameters()  ,						lr=1E-3       )
										__SCREAMING_SNAKE_CASE						       =    AdamW(params=ddp_model.parameters()  ,						lr=1E-3       )
										__SCREAMING_SNAKE_CASE						       =    LambdaLR(lowerCAmelCase_  ,						lr_lambda=lambda lowerCAmelCase_       : epoch**0.65       )
										__SCREAMING_SNAKE_CASE						       =    LambdaLR(lowerCAmelCase_  ,						lr_lambda=lambda lowerCAmelCase_       : epoch**0.65       )
					# Make a copy of `model`
					if sched:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    accelerator.prepare(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					else:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    accelerator.prepare(lowerCAmelCase_  ,						lowerCAmelCase_       )
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
										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       )
										__SCREAMING_SNAKE_CASE						       =    ddp_input[torch.randperm(len(lowerCAmelCase_       )       )]




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

										# 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       )
										__SCREAMING_SNAKE_CASE						       =    ddp_input[torch.randperm(len(lowerCAmelCase_       )       )]




def        UpperCAmelCase__	(lowerCAmelCase_=False  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


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

										# 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(lowerCAmelCase_       ) - 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       )
										__SCREAMING_SNAKE_CASE						       =    ddp_input[torch.randperm(len(lowerCAmelCase_       )       )]
					GradientState._reset_state()




def        UpperCAmelCase__	(lowerCAmelCase_=False  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    Accelerator(
					    split_batches=lowerCAmelCase_  ,						dispatch_batches=lowerCAmelCase_  ,						gradient_accumulation_steps=2       )
					# Test that context manager behaves properly
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    get_training_setup(lowerCAmelCase_  ,						lowerCAmelCase_       )
					for iteration, batch in enumerate(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    batch.values()
										# Gather the distributed inputs and targs for the base model
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    accelerator.gather((ddp_input, ddp_target)       )
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_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(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase_       )):
															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(lowerCAmelCase_       ):
															step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
															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"""
										__SCREAMING_SNAKE_CASE						       =    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase_       ))
										if accelerator.num_processes > 1:
															check_model_parameters(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration       )
					GradientState._reset_state()




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    Accelerator()

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




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    Accelerator()
					__SCREAMING_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(lowerCAmelCase_       )
					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(lowerCAmelCase_       )
					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(lowerCAmelCase_  ,						lowerCAmelCase_       )

    # 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(lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [0 for i in range(r + 1       )]
					# nc0 = 1
					__SCREAMING_SNAKE_CASE						       =    1
					for i in range(1  ,						n + 1       ):
										# to compute current row from previous row.
										__SCREAMING_SNAKE_CASE						       =    min(lowerCAmelCase_  ,						lowerCAmelCase_       )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]



print(binomial_coefficient(n=1_0, r=5))
