




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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=True )			->							List[str]:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							str:


			'''simple docstring'''







			set_seed(42 )
			SCREAMING_SNAKE_CASE_								=						RegressionModel()
			SCREAMING_SNAKE_CASE_								=						deepcopy(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						RegressionDataset(length=80 )
			SCREAMING_SNAKE_CASE_								=						DataLoader(SCREAMING_SNAKE_CASE       ,    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(SCREAMING_SNAKE_CASE       ,    lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
						SCREAMING_SNAKE_CASE_								=						LambdaLR(SCREAMING_SNAKE_CASE       ,    lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
			# Make a copy of `model`
			if sched:
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						accelerator.prepare(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			else:
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						accelerator.prepare(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						get_training_setup(SCREAMING_SNAKE_CASE )
			# Use a single batch
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						next(iter(SCREAMING_SNAKE_CASE ) ).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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
												step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						else:
									# Sync grads
									step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_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(13_37 + iteration )
						SCREAMING_SNAKE_CASE_								=						ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						get_training_setup(SCREAMING_SNAKE_CASE )
			# Use a single batch
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						next(iter(SCREAMING_SNAKE_CASE ) ).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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
												step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						else:
									# Sync grads
									step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_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(13_37 + iteration )
						SCREAMING_SNAKE_CASE_								=						ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]

def lowercase    (  SCREAMING_SNAKE_CASE=False       ,    SCREAMING_SNAKE_CASE=False )			->							Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE       ,    dispatch_batches=SCREAMING_SNAKE_CASE       ,    gradient_accumulation_steps=2 )
			# Test that context manager behaves properly
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						get_training_setup(SCREAMING_SNAKE_CASE )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
						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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
									step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_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(SCREAMING_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(13_37 + iteration )
						SCREAMING_SNAKE_CASE_								=						ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
			GradientState._reset_state()

def lowercase    (  SCREAMING_SNAKE_CASE=False       ,    SCREAMING_SNAKE_CASE=False )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE       ,    dispatch_batches=SCREAMING_SNAKE_CASE       ,    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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
						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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_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(SCREAMING_SNAKE_CASE ):
									step_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_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'
						SCREAMING_SNAKE_CASE_								=						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE ))
						if accelerator.num_processes > 1:
									check_model_parameters(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(13_37 + iteration )
			GradientState._reset_state()

def lowercase    (  )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Accelerator()

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


			'''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(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE       ,    SCREAMING_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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()


if __name__ == "__main__":
				main()






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
