






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A        =			logging.get_logger(__name__)

A        =			{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class 							__a							(  __A ,	__A						):





		'''simple docstring'''



		UpperCAmelCase__      :				int				    =						"""nat"""

		UpperCAmelCase__      :				Tuple				    =						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}


		def __init__(						self ,    UpperCamelCase__=4 ,    UpperCamelCase__=3 ,    UpperCamelCase__=64 ,    UpperCamelCase__=[3, 4, 6, 5] ,    UpperCamelCase__=[2, 4, 8, 16] ,    UpperCamelCase__=7 ,    UpperCamelCase__=3.0 ,    UpperCamelCase__=True ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=1E-5 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   patch_size
						SCREAMING_SNAKE_CASE_   :	str													=   num_channels
						SCREAMING_SNAKE_CASE_   :	List[str]													=   embed_dim
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   depths
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   len(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   num_heads
						SCREAMING_SNAKE_CASE_   :	Dict													=   kernel_size
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   mlp_ratio
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   qkv_bias
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE_   :	List[str]													=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   drop_path_rate
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Tuple													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   initializer_range
						# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   int(embed_dim * 2 ** (len(UpperCamelCase__       ) - 1)       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   layer_scale_init_value
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ['stem'] + [F'''stage{idx}''' for idx in range(1 ,    len(UpperCamelCase__       ) + 1       )]
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_aligned_output_features_output_indices(
						    out_features=UpperCamelCase__ ,    out_indices=UpperCamelCase__ ,    stage_names=self.stage_names       )







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__				:		int    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''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(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[Any]=True      ):





				'''simple docstring'''







				model.train()
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	List[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__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				set_seed(42      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   RegressionModel()
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   deepcopy(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				model.to(accelerator.device      )
				if sched:
								SCREAMING_SNAKE_CASE_   :	Dict													=   AdamW(params=model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AdamW(params=ddp_model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   LambdaLR(lowerCAmelCase__    ,    lr_lambda=lambda lowerCAmelCase__      : epoch**0.65      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   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_   :	Optional[int]													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   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__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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_   :	int													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	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__      )

								# 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_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   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_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   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_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]=False    ,    lowerCAmelCase__				:		Union[str, Any]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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_   :	Union[str, Any]													=   get_training_setup(lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   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_   :	str													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]
				GradientState._reset_state()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int=False    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   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_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   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_   :	Optional[int]													=   (((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(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Accelerator()

				SCREAMING_SNAKE_CASE_   :	int													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   RegressionDataset(length=96      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   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(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   Accelerator()
				SCREAMING_SNAKE_CASE_   :	str													=   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__				:		Optional[int]      ):





				'''simple docstring'''







				main()


if __name__ == "__main__":
				main()