"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=2						,__UpperCAmelCase=True						,__UpperCAmelCase=False						,__UpperCAmelCase=10						,__UpperCAmelCase=3						,__UpperCAmelCase=32 * 8						,__UpperCAmelCase=32 * 8						,__UpperCAmelCase=4						,__UpperCAmelCase=64						,)    -> Dict:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							is_training
													A__		   =							use_auxiliary_loss
													A__		   =							num_queries
													A__		   =							num_channels
													A__		   =							min_size
													A__		   =							max_size
													A__		   =							num_labels
													A__		   =							hidden_dim
													A__		   =							hidden_dim





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
													    __UpperCAmelCase   )

													A__		   =							torch.ones([self.batch_size, self.min_size, self.max_size]						,device=__UpperCAmelCase   )

													A__		   =							(
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,device=__UpperCAmelCase   ) > 0.5
													).float()
													A__		   =							(torch.rand((self.batch_size, self.num_labels)						,device=__UpperCAmelCase   ) > 0.5).long()

													A__		   =							self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							MaskaFormerConfig(
													    hidden_size=self.hidden_dim						,)
													A__		   =							self.num_queries
													A__		   =							self.num_labels

													A__		   =							[1, 1, 1, 1]
													A__		   =							self.num_channels

													A__		   =							64
													A__		   =							1_28
													A__		   =							self.hidden_dim
													A__		   =							self.hidden_dim
													A__		   =							self.hidden_dim
													return config





						def  snake_case__		(				self   )    -> List[str]:
													A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							self.prepare_config_and_inputs()
													A__		   =							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
													return config, inputs_dict





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							output.encoder_hidden_states
													A__		   =							output.pixel_decoder_hidden_states
													A__		   =							output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(__UpperCAmelCase   )						,len(config.backbone_config.depths   )   )
													self.parent.assertTrue(len(__UpperCAmelCase   )						,len(config.backbone_config.depths   )   )
													self.parent.assertTrue(len(__UpperCAmelCase   )						,config.decoder_layers   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> List[str]:
													with torch.no_grad():
																				A__		   =							MaskaFormerModel(config=__UpperCAmelCase   )
																				model.to(__UpperCAmelCase   )
																				model.eval()

																				A__		   =							model(pixel_values=__UpperCAmelCase						,pixel_mask=__UpperCAmelCase   )
																				A__		   =							model(__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase   )

													self.parent.assertEqual(
													    output.transformer_decoder_last_hidden_state.shape						,(self.batch_size, self.num_queries, self.hidden_dim)						,)
													# let's ensure the other two hidden state exists
													self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
													self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

													if output_hidden_states:
																				self.check_output_hidden_state(__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()

													def comm_check_on_output(__UpperCAmelCase   ):
																				# let's still check that all the required stuff is there
																				self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
																				self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
																				self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
																				# okay, now we need to check the logits shape
																				# due to the encoder compression, masks have a //4 spatial size
																				self.parent.assertEqual(
																				    result.masks_queries_logits.shape						,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,)
																				# + 1 for null class
																				self.parent.assertEqual(
																				    result.class_queries_logits.shape						,(self.batch_size, self.num_queries, self.num_labels + 1)   )

													with torch.no_grad():
																				A__		   =							model(pixel_values=__UpperCAmelCase						,pixel_mask=__UpperCAmelCase   )
																				A__		   =							model(__UpperCAmelCase   )

																				comm_check_on_output(__UpperCAmelCase   )

																				A__		   =							model(
																				    pixel_values=__UpperCAmelCase						,pixel_mask=__UpperCAmelCase						,mask_labels=__UpperCAmelCase						,class_labels=__UpperCAmelCase   )

													comm_check_on_output(__UpperCAmelCase   )

													self.parent.assertTrue(result.loss is not None   )
													self.parent.assertEqual(result.loss.shape						,torch.Size([1]   )   )






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Optional[int]  							=						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
						lowerCAmelCase__     :  Tuple  							=						{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

						lowerCAmelCase__     :  List[Any]  							=						False
						lowerCAmelCase__     :  List[Any]  							=						False
						lowerCAmelCase__     :  int  							=						False
						lowerCAmelCase__     :  Dict  							=						False





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							MaskaFormerModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													self.config_tester.run_common_tests()





						def  snake_case__		(				self   )    -> Optional[int]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase						,**__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCAmelCase   )





						@unittest.skip(reason='Mask2Former does not use inputs_embeds'   )
						def  snake_case__		(				self   )    -> Optional[int]:
													pass





						@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'   )
						def  snake_case__		(				self   )    -> List[Any]:
													pass





						@unittest.skip(reason='Mask2Former is not a generative model'   )
						def  snake_case__		(				self   )    -> int:
													pass





						@unittest.skip(reason='Mask2Former does not use token embeddings'   )
						def  snake_case__		(				self   )    -> int:
													pass





						@require_torch_multi_gpu
						@unittest.skip(
						    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'   )
						def  snake_case__		(				self   )    -> List[str]:
													pass





						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
						def  snake_case__		(				self   )    -> Union[str, Any]:
													pass





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						@slow
						def  snake_case__		(				self   )    -> Optional[int]:
													for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																				A__		   =							MaskaFormerModel.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							(self.model_tester.min_size,) * 2
													A__		   =							{
													    'pixel_values': torch.randn((2, 3, *size)						,device=__UpperCAmelCase   ),
													    'mask_labels': torch.randn((2, 10, *size)						,device=__UpperCAmelCase   ),
													    'class_labels': torch.zeros(2						,10						,device=__UpperCAmelCase   ).long(),
													}
													A__		   =							self.model_tester.get_config()

													A__		   =							MaskaFormerForUniversalSegmentation(__UpperCAmelCase   ).to(__UpperCAmelCase   )
													A__		   =							model(**__UpperCAmelCase   )
													self.assertTrue(outputs.loss is not None   )





						def  snake_case__		(				self   )    -> str:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase						,**__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							model_class(__UpperCAmelCase   ).to(__UpperCAmelCase   )
																				A__		   =							model(**__UpperCAmelCase						,output_attentions=__UpperCAmelCase   )
																				self.assertTrue(outputs.attentions is not None   )





						def  snake_case__		(				self   )    -> str:
													if not self.model_tester.is_training:
																				return

													A__		   =							self.all_model_classes[1]
													A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs()

													A__		   =							model_class(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.train()

													A__		   =							model(__UpperCAmelCase						,mask_labels=__UpperCAmelCase						,class_labels=__UpperCAmelCase   ).loss
													loss.backward()





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.all_model_classes[1]
													A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs()
													A__		   =							True
													A__		   =							True

													A__		   =							model_class(__UpperCAmelCase   ).to(__UpperCAmelCase   )
													model.train()

													A__		   =							model(__UpperCAmelCase						,mask_labels=__UpperCAmelCase						,class_labels=__UpperCAmelCase   )

													A__		   =							outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													A__		   =							outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()

													A__		   =							outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													A__		   =							outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=__UpperCAmelCase   )

													self.assertIsNotNone(encoder_hidden_states.grad   )
													self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
													self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
													self.assertIsNotNone(attentions.grad   )






__lowerCamelCase							 =     1E-4






def      UpperCAmelCase    (		):






							"""simple docstring"""





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


@require_vision
@slow
class 			UpperCamelCase__(	unittest.TestCase       ):





						@cached_property
						def  snake_case__		(				self   )    -> int:
													return "facebook/mask2former-swin-small-coco-instance"





						@cached_property
						def  snake_case__		(				self   )    -> Tuple:
													return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None





						def  snake_case__		(				self   )    -> str:
													A__		   =							MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(__UpperCAmelCase   )
													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )
													A__		   =							inputs['pixel_values'].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
													# check size
													self.assertEqual(__UpperCAmelCase						,(1, 3, 3_84, 3_84)   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]   ).to(__UpperCAmelCase   )
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,__UpperCAmelCase						,atol=__UpperCAmelCase   )   )

													A__		   =							torch.tensor(
													    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]   ).to(__UpperCAmelCase   )
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,__UpperCAmelCase						,atol=__UpperCAmelCase   )   )

													A__		   =							torch.tensor(
													    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]   ).to(__UpperCAmelCase   )
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,__UpperCAmelCase						,atol=__UpperCAmelCase   )   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__UpperCAmelCase   ).eval()
													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )
													A__		   =							inputs['pixel_values'].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
													# check size
													self.assertEqual(__UpperCAmelCase						,(1, 3, 3_84, 3_84)   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )
													# masks_queries_logits
													A__		   =							outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape						,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
													A__		   =							[
													    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
													    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
													    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
													]
													A__		   =							torch.tensor(__UpperCAmelCase   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=__UpperCAmelCase   )   )
													# class_queries_logits
													A__		   =							outputs.class_queries_logits
													self.assertEqual(class_queries_logits.shape						,(1, model.config.num_queries, model.config.num_labels + 1)   )
													A__		   =							torch.tensor(
													    [
													        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
													        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
													        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
													    ]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,__UpperCAmelCase						,atol=__UpperCAmelCase   )   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__UpperCAmelCase   ).eval()
													A__		   =							self.default_image_processor

													A__		   =							image_processor(
													    [np.zeros((3, 8_00, 13_33)   ), np.zeros((3, 8_00, 13_33)   )]						,segmentation_maps=[np.zeros((3_84, 3_84)   ).astype(np.floataa   ), np.zeros((3_84, 3_84)   ).astype(np.floataa   )]						,return_tensors='pt'						,)

													A__		   =							inputs['pixel_values'].to(__UpperCAmelCase   )
													A__		   =							[el.to(__UpperCAmelCase   ) for el in inputs['mask_labels']]
													A__		   =							[el.to(__UpperCAmelCase   ) for el in inputs['class_labels']]

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

													self.assertTrue(outputs.loss is not None   )






"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=True ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														for param, ddp_param in zip(model.parameters()				,			ddp_model.parameters() ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad				,			ddp_param.grad ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()				,			ddp_model.parameters() ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad				,			ddp_param.grad ) is False
																												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad				,			ddp_param.grad ) is True
																												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





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

														# 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(UpperCamelCase__ ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad				,			ddp_param.grad ) is True
																												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad				,			ddp_param.grad ) is False
																												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





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

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__ )):
																					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(UpperCamelCase__ ):
																					step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
														A__		   =							(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__ ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration )
							GradientState._reset_state()






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()

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






							"""simple docstring"""





							A__		   =							Accelerator()
							A__		   =							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(UpperCamelCase__ )
							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(UpperCamelCase__ )
							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(UpperCamelCase__				,			UpperCamelCase__ )

    # 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(UpperCamelCase__				,			UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
				main()





