





'''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  a__    (    lowercase :					List[str], lowercase :					Tuple, lowercase :					Union[str, Any], lowercase :					str				)		->		Union[str, 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  a__    (    lowercase :					List[str], lowercase :					Optional[Any], lowercase :					Union[str, Any], lowercase :					int, lowercase :					Dict=True				)		->		List[str]:





					"""simple docstring"""

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







def  a__    (    lowercase :					int, lowercase :					Optional[Any]=False				)		->		Union[str, Any]:





					"""simple docstring"""

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







def  a__    (    lowercase :					Optional[Any]				)		->		Optional[Any]:





					"""simple docstring"""

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

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







def  a__    (    lowercase :					Optional[int]				)		->		Dict:





					"""simple docstring"""

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

										# 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				)
										_UpperCamelCase       =	ddp_input[torch.randperm(len(lowercase				)				)]







def  a__    (    lowercase :					int=False, lowercase :					List[str]=False				)		->		List[str]:





					"""simple docstring"""

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

										# 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(lowercase				) - 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				)
										_UpperCamelCase       =	ddp_input[torch.randperm(len(lowercase				)				)]
					GradientState._reset_state()







def  a__    (    lowercase :					Tuple=False, lowercase :					Dict=False				)		->		Dict:





					"""simple docstring"""

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

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







def  a__    (    )		->		Optional[Any]:





					"""simple docstring"""

					_UpperCamelCase       =	Accelerator()

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





					"""simple docstring"""

					_UpperCamelCase       =	Accelerator()
					_UpperCamelCase       =	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(lowercase				)
					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(lowercase				)
					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(lowercase, lowercase				)

    # 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(lowercase, lowercase				)







def  a__    (    lowercase :					int				)		->		List[Any]:





					"""simple docstring"""

					main()


if __name__ == "__main__":
			main()










'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Tuple					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                # Removed: 'unet/diffusion_pytorch_model.safetensors',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                # Removed: 'text_encoder/model.safetensors',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                # 'text_encoder/model.fp16.safetensors',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)



