






import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
				from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor


class 							__a							:





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=13 ,    UpperCamelCase__=32 ,    UpperCamelCase__=3 ,    UpperCamelCase__=4 ,    UpperCamelCase__=[10, 20, 30, 40] ,    UpperCamelCase__=[2, 2, 3, 2] ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__=37 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=10 ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=["stage2", "stage3", "stage4"] ,    UpperCamelCase__=[2, 3, 4] ,    UpperCamelCase__=None ,    ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   parent
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch_size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   image_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   num_channels
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   num_stages
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_sizes
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   depths
						SCREAMING_SNAKE_CASE_   :	int													=   is_training
						SCREAMING_SNAKE_CASE_   :	Any													=   use_labels
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   num_labels
						SCREAMING_SNAKE_CASE_   :	Dict													=   initializer_range
						SCREAMING_SNAKE_CASE_   :	Dict													=   out_features
						SCREAMING_SNAKE_CASE_   :	List[str]													=   out_indices
						SCREAMING_SNAKE_CASE_   :	int													=   scope


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   ids_tensor([self.batch_size] ,    self.num_labels       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_config()
						return config, pixel_values, labels


		def   __snake_case			(						self       ):
						return ConvNextConfig(
						    num_channels=self.num_channels ,    hidden_sizes=self.hidden_sizes ,    depths=self.depths ,    num_stages=self.num_stages ,    hidden_act=self.hidden_act ,    is_decoder=_a ,    initializer_range=self.initializer_range ,    out_features=self.out_features ,    out_indices=self.out_indices ,    num_labels=self.num_labels ,    )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   ConvNextModel(config=_a       )
						model.to(_a       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(_a       )
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,    )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ConvNextForImageClassification(_a       )
						model.to(_a       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Dict													=   model(_a ,    labels=_a       )
						self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	str													=   ConvNextBackbone(config=_a       )
						model.to(_a       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   model(_a       )

						# verify hidden states
						self.parent.assertEqual(len(result.feature_maps       ) ,    len(config.out_features       )       )
						self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,    [self.batch_size, self.hidden_sizes[1], 4, 4]       )

						# verify channels
						self.parent.assertEqual(len(model.channels       ) ,    len(config.out_features       )       )
						self.parent.assertListEqual(model.channels ,    config.hidden_sizes[1:]       )

						# verify backbone works with out_features=None
						SCREAMING_SNAKE_CASE_   :	Dict													=   None
						SCREAMING_SNAKE_CASE_   :	Dict													=   ConvNextBackbone(config=_a       )
						model.to(_a       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   model(_a       )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps       ) ,    1       )
						self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,    [self.batch_size, self.hidden_sizes[-1], 1, 1]       )

						# verify channels
						self.parent.assertEqual(len(model.channels       ) ,    1       )
						self.parent.assertListEqual(model.channels ,    [config.hidden_sizes[-1]]       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   config_and_inputs
						SCREAMING_SNAKE_CASE_   :	Tuple													=   {"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
class 							__a							(  UpperCamelCase__ ,	UpperCamelCase__ ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]				    =						(
		    (
		        ConvNextModel,
		        ConvNextForImageClassification,
		        ConvNextBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase__      :				Tuple				    =						(
		    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase__      :				Tuple				    =						True
		UpperCAmelCase__      :				List[Any]				    =						False
		UpperCAmelCase__      :				List[Any]				    =						False
		UpperCAmelCase__      :				Union[str, Any]				    =						False
		UpperCAmelCase__      :				int				    =						False


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ConvNextModelTester(self       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ConfigTester(self ,    config_class=_a ,    has_text_modality=_a ,    hidden_size=37       )


		def   __snake_case			(						self       ):
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()


		def   __snake_case			(						self       ):
						return


		@unittest.skip(reason='ConvNext does not use inputs_embeds'       )
		def   __snake_case			(						self       ):
						pass


		@unittest.skip(reason='ConvNext does not support input and output embeddings'       )
		def   __snake_case			(						self       ):
						pass


		@unittest.skip(reason='ConvNext does not use feedforward chunking'       )
		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   :	Any													=   model_class(_a       )
										SCREAMING_SNAKE_CASE_   :	Any													=   inspect.signature(model.forward       )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_   :	List[str]													=   ["""pixel_values"""]
										self.assertListEqual(arg_names[:1] ,    _a       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_a       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*_a       )


		def   __snake_case			(						self       ):
						def check_hidden_states_output(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   model_class(_a       )
										model.to(_a       )
										model.eval()

										with torch.no_grad():
														SCREAMING_SNAKE_CASE_   :	str													=   model(**self._prepare_for_class(_a ,    _a       )       )

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										SCREAMING_SNAKE_CASE_   :	Dict													=   self.model_tester.num_stages
										self.assertEqual(len(_a       ) ,    expected_num_stages + 1       )

										# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]       ) ,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,    )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   True
										check_hidden_states_output(_a ,    _a ,    _a       )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   True

										check_hidden_states_output(_a ,    _a ,    _a       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_a       )






		@slow
		def   __snake_case			(						self       ):
						for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ConvNextModel.from_pretrained(_a       )
										self.assertIsNotNone(_a       )






def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image


@require_torch
@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		@cached_property
		def   __snake_case			(						self       ):
						return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'       ) if is_vision_available() else None






		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'       ).to(_a       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.default_image_processor
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   prepare_img()
						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(images=_a ,    return_tensors='pt'       ).to(_a       )

						# forward pass
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Tuple													=   model(**_a       )

						# verify the logits
						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.Size((1, 1000)       )
						self.assertEqual(outputs.logits.shape ,    _a       )

						SCREAMING_SNAKE_CASE_   :	str													=   torch.tensor([-0.02_60, -0.47_39, 0.19_11]       ).to(_a       )

						self.assertTrue(torch.allclose(outputs.logits[0, :3] ,    _a ,    atol=1E-4       )       )






@require_torch
class 							__a							(  unittest.TestCase ,	UpperCamelCase__						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]				    =						(ConvNextBackbone,) if is_torch_available() else ()
		UpperCAmelCase__      :				List[Any]				    =						ConvNextConfig

		UpperCAmelCase__      :				Tuple				    =						False


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ConvNextModelTester(self       )







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()