




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_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				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_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=True   ):
				model.train()
				__magic_name__	:	Tuple								=model(lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=F.mse_loss(lowerCamelCase	,     target.to(output.device   )   )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				set_seed(42   )
				__magic_name__	:	Optional[int]								=RegressionModel()
				__magic_name__	:	Optional[int]								=deepcopy(lowerCamelCase   )
				__magic_name__	:	Dict								=RegressionDataset(length=80   )
				__magic_name__	:	List[Any]								=DataLoader(lowerCamelCase	,     batch_size=16   )
				model.to(accelerator.device   )
				if sched:
								__magic_name__	:	Union[str, Any]								=AdamW(params=model.parameters()	,     lr=1E-3   )
								__magic_name__	:	Tuple								=AdamW(params=ddp_model.parameters()	,     lr=1E-3   )
								__magic_name__	:	Optional[Any]								=LambdaLR(lowerCamelCase	,     lr_lambda=lambda lowerCamelCase   : epoch**0.6_5   )
								__magic_name__	:	Dict								=LambdaLR(lowerCamelCase	,     lr_lambda=lambda lowerCamelCase   : epoch**0.6_5   )
				# Make a copy of `model`
				if sched:
								__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	int								=accelerator.prepare(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				else:
								__magic_name__				, __magic_name__	:	Tuple								=accelerator.prepare(lowerCamelCase	,     lowerCamelCase   )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# Test when on a single CPU or GPU that the context manager does nothing
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=get_training_setup(lowerCamelCase   )
				# Use a single batch
				__magic_name__				, __magic_name__	:	Dict								=next(iter(lowerCamelCase   )   ).values()
				for iteration in range(3   ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__				, __magic_name__	:	Tuple								=accelerator.gather((ddp_input, ddp_target)   )
								__magic_name__				, __magic_name__	:	str								=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   )
								__magic_name__	:	Optional[int]								=ddp_input[torch.randperm(len(lowerCamelCase   )   )]




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# Test on distributed setup that context manager behaves properly
				__magic_name__				, __magic_name__				, __magic_name__	:	Any								=get_training_setup(lowerCamelCase   )
				# Use a single batch
				__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(lowerCamelCase   )   ).values()
				for iteration in range(3   ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__				, __magic_name__	:	Tuple								=accelerator.gather((ddp_input, ddp_target)   )
								__magic_name__				, __magic_name__	:	Union[str, Any]								=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   )
								__magic_name__	:	str								=ddp_input[torch.randperm(len(lowerCamelCase   )   )]




def 		lowerCAmelCase_   (	lowerCamelCase=False	,     lowerCamelCase=False   ):
				__magic_name__	:	Any								=Accelerator(
				    split_batches=lowerCamelCase	,     dispatch_batches=lowerCamelCase	,     gradient_accumulation_steps=2   )
				# Test that context manager behaves properly
				__magic_name__				, __magic_name__				, __magic_name__	:	Any								=get_training_setup(lowerCamelCase   )
				for iteration, batch in enumerate(lowerCamelCase   ):
								__magic_name__				, __magic_name__	:	Tuple								=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__				, __magic_name__	:	Any								=accelerator.gather((ddp_input, ddp_target)   )
								__magic_name__				, __magic_name__	:	List[Any]								=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   )
								__magic_name__	:	int								=ddp_input[torch.randperm(len(lowerCamelCase   )   )]
				GradientState._reset_state()




def 		lowerCAmelCase_   (	lowerCamelCase=False	,     lowerCamelCase=False   ):
				__magic_name__	:	List[Any]								=Accelerator(
				    split_batches=lowerCamelCase	,     dispatch_batches=lowerCamelCase	,     gradient_accumulation_steps=2   )
				# Test that context manager behaves properly
				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Dict								=get_training_setup(lowerCamelCase	,     lowerCamelCase   )
				for iteration, batch in enumerate(lowerCamelCase   ):
								__magic_name__				, __magic_name__	:	List[Any]								=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__				, __magic_name__	:	Dict								=accelerator.gather((ddp_input, ddp_target)   )
								__magic_name__				, __magic_name__	:	Union[str, Any]								=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"
								__magic_name__	:	List[Any]								=(((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 		lowerCAmelCase_   (	):
				__magic_name__	:	Dict								=Accelerator()

				__magic_name__	:	Optional[int]								=RegressionDataset(length=80   )
				__magic_name__	:	Dict								=DataLoader(lowerCamelCase	,     batch_size=16   )
				__magic_name__	:	int								=RegressionDataset(length=96   )
				__magic_name__	:	List[str]								=DataLoader(lowerCamelCase	,     batch_size=16   )
				__magic_name__				, __magic_name__	:	str								=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 		lowerCAmelCase_   (	):
				__magic_name__	:	Dict								=Accelerator()
				__magic_name__	:	List[Any]								=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 		lowerCAmelCase_   (	lowerCamelCase   ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()










from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

UpperCAmelCase_		:				Dict									=     637_8137.0
UpperCAmelCase_		:				List[Any]									=     635_6752.31_4245
UpperCAmelCase_		:				List[str]									=     6378137




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	str								=(AXIS_A - AXIS_B) / AXIS_A
				# Parametric latitudes
				# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
				__magic_name__	:	str								=atan((1 - flattening) * tan(radians(lowerCamelCase   )   )   )
				__magic_name__	:	List[Any]								=atan((1 - flattening) * tan(radians(lowerCamelCase   )   )   )

				# Compute central angle between two points
				# using haversine theta. sigma =  haversine_distance / equatorial radius
				__magic_name__	:	List[Any]								=haversine_distance(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ) / EQUATORIAL_RADIUS

				# Intermediate P and Q values
				__magic_name__	:	Tuple								=(b_lata + b_lata) / 2
				__magic_name__	:	int								=(b_lata - b_lata) / 2

				# Intermediate X value
				# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
				__magic_name__	:	Optional[int]								=(sin(lowerCamelCase   ) ** 2) * (cos(lowerCamelCase   ) ** 2)
				__magic_name__	:	Any								=cos(sigma / 2   ) ** 2
				__magic_name__	:	List[Any]								=(sigma - sin(lowerCamelCase   )) * (x_numerator / x_demonimator)

				# Intermediate Y value
				# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
				__magic_name__	:	Any								=(cos(lowerCamelCase   ) ** 2) * (sin(lowerCamelCase   ) ** 2)
				__magic_name__	:	Optional[Any]								=sin(sigma / 2   ) ** 2
				__magic_name__	:	str								=(sigma + sin(lowerCamelCase   )) * (y_numerator / y_denominator)

				return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




