

'''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	(       __snake_case					:		Optional[Any],		__snake_case					:		Any,		__snake_case					:		Dict,		__snake_case					:		int	)	->							str:






						"""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	(       __snake_case					:		Dict,		__snake_case					:		Tuple,		__snake_case					:		Union[str, Any],		__snake_case					:		List[Any],		__snake_case					:		int=True	)	->							int:






						"""simple docstring"""





						model.train()
						A__					:       List[Any]     =model(__snake_case	)
						A__					:       Dict     =F.mse_loss(__snake_case,		target.to(output.device	)	)
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		List[str],		__snake_case					:		Optional[Any]=False	)	->							Dict:






						"""simple docstring"""





						set_seed(42	)
						A__					:       List[str]     =RegressionModel()
						A__					:       int     =deepcopy(__snake_case	)
						A__					:       Dict     =RegressionDataset(length=80	)
						A__					:       Optional[int]     =DataLoader(__snake_case,		batch_size=16	)
						model.to(accelerator.device	)
						if sched:
												A__					:       List[str]     =AdamW(params=model.parameters(),		lr=1E-3	)
												A__					:       Any     =AdamW(params=ddp_model.parameters(),		lr=1E-3	)
												A__					:       Union[str, Any]     =LambdaLR(__snake_case,		lr_lambda=lambda __snake_case	: epoch**0.65	)
												A__					:       List[str]     =LambdaLR(__snake_case,		lr_lambda=lambda __snake_case	: epoch**0.65	)
						# Make a copy of `model`
						if sched:
												A__   ,						A__   ,						A__   ,						A__					:       Dict     =accelerator.prepare(__snake_case,		__snake_case,		__snake_case,		__snake_case	)
						else:
												A__   ,						A__					:       int     =accelerator.prepare(__snake_case,		__snake_case	)
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader
def 						__lowerCamelCase	(       __snake_case					:		List[str]	)	->							int:






						"""simple docstring"""





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

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(__snake_case,		__snake_case,		__snake_case,		__snake_case	)
												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(1_337 + iteration	)
												A__					:       Dict     =ddp_input[torch.randperm(len(__snake_case	)	)]
def 						__lowerCamelCase	(       __snake_case					:		Optional[Any]	)	->							Dict:






						"""simple docstring"""





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

												# 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(1_337 + iteration	)
												A__					:       Any     =ddp_input[torch.randperm(len(__snake_case	)	)]
def 						__lowerCamelCase	(       __snake_case					:		List[Any]=False,		__snake_case					:		Optional[int]=False	)	->							List[Any]:






						"""simple docstring"""





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

												# 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(__snake_case	) - 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(1_337 + iteration	)
												A__					:       Any     =ddp_input[torch.randperm(len(__snake_case	)	)]
						GradientState._reset_state()
def 						__lowerCamelCase	(       __snake_case					:		Optional[Any]=False,		__snake_case					:		Tuple=False	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       Optional[int]     =Accelerator(
						    split_batches=__snake_case,		dispatch_batches=__snake_case,		gradient_accumulation_steps=2	)
						# Test that context manager behaves properly
						A__   ,						A__   ,						A__   ,						A__   ,						A__   ,						A__   ,						A__					:       Optional[int]     =get_training_setup(__snake_case,		__snake_case	)
						for iteration, batch in enumerate(__snake_case	):
												A__   ,						A__					:       Any     =batch.values()
												# Gather the distributed inputs and targs for the base model
												A__   ,						A__					:       Optional[int]     =accelerator.gather((ddp_input, ddp_target)	)
												A__   ,						A__					:       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(__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case	)
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case	)):
																		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(__snake_case	):
																		step_model(__snake_case,		__snake_case,		__snake_case,		__snake_case	)
																		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"
												A__					:       Any     =(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case	))
												if accelerator.num_processes > 1:
																		check_model_parameters(__snake_case,		__snake_case,		__snake_case,		__snake_case	)
												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_337 + iteration	)
						GradientState._reset_state()
def 						__lowerCamelCase	(       )	->							Union[str, Any]:






						"""simple docstring"""





						A__					:       Any     =Accelerator()

						A__					:       List[Any]     =RegressionDataset(length=80	)
						A__					:       Optional[Any]     =DataLoader(__snake_case,		batch_size=16	)
						A__					:       List[str]     =RegressionDataset(length=96	)
						A__					:       Optional[Any]     =DataLoader(__snake_case,		batch_size=16	)
						A__   ,						A__					:       Optional[Any]     =accelerator.prepare(__snake_case,		__snake_case	)
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(__snake_case	):
												assert id(accelerator.gradient_state.active_dataloader	) == id(__snake_case	)
												if iteration < len(__snake_case	) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(__snake_case	):
																														assert id(accelerator.gradient_state.active_dataloader	) == id(__snake_case	)
																														if batch_num < len(__snake_case	) - 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	(       )	->							Union[str, Any]:






						"""simple docstring"""





						A__					:       Union[str, Any]     =Accelerator()
						A__					:       Optional[int]     =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(__snake_case	)
						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(__snake_case	)
						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(__snake_case,		__snake_case	)

    # 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(__snake_case,		__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		Dict	)	->							List[Any]:






						"""simple docstring"""





						main()


if __name__ == "__main__":
		main()




'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp



@slow
@require_flax
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]			)      ->		List[str]:





									'''simple docstring'''
									return f"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_			) for s in shape]			)}.npy"


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						str=0		,      lowerCAmelCase_	:						List[str]=(4, 4, 64, 64)		,      lowerCAmelCase_	:						Optional[Any]=False			)      ->		str:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Any     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return image


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Optional[int]=False		,      lowerCAmelCase_	:						Dict="CompVis/stable-diffusion-v1-4"			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Any     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       int     ="""bf16""" if fpaa else None

									A__   ,						A__					:       Any     =FlaxUNetaDConditionModel.from_pretrained(
									    lowerCAmelCase_		,      subfolder="""unet"""		,      dtype=lowerCAmelCase_		,      revision=lowerCAmelCase_			)
									return model, params


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int=0		,      lowerCAmelCase_	:						Optional[Any]=(4, 77, 7_68)		,      lowerCAmelCase_	:						List[str]=False			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Optional[int]     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return hidden_states


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		int:





									'''simple docstring'''
									A__   ,						A__					:       Tuple     =self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,      fpaa=lowerCAmelCase_			)
									A__					:       List[Any]     =self.get_latents(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)
									A__					:       Optional[int]     =self.get_encoder_hidden_states(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[int]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       Tuple     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)



			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[int]			)      ->		List[str]:





									'''simple docstring'''
									A__   ,						A__					:       List[str]     =self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,      fpaa=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =self.get_latents(lowerCAmelCase_		,      shape=(4, 4, 96, 96)		,      fpaa=lowerCAmelCase_			)
									A__					:       Dict     =self.get_encoder_hidden_states(lowerCAmelCase_		,      shape=(4, 77, 10_24)		,      fpaa=lowerCAmelCase_			)

									A__					:       Optional[Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[Any]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       List[Any]     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)

