



import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


A__  :  Any								=					logging.getLogger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any]=2     ,__UpperCamelCase							:		Optional[Any]=3     ,__UpperCamelCase							:		Dict=16     ,__UpperCamelCase							:		int = 10     ,__UpperCamelCase							:		int = 2 ):

		def get_dataset(__UpperCamelCase							:		Union[str, Any] ):
				lowerCAmelCase_			:     Any									= torch.randn(batch_size * n_batches     ,1 )
				return TensorDataset(__UpperCamelCase     ,a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,1 ) )

		lowerCAmelCase_			:     Dict									= get_dataset(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= get_dataset(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= DataLoader(__UpperCamelCase     ,shuffle=__UpperCamelCase     ,batch_size=__UpperCamelCase     ,num_workers=4 )
		lowerCAmelCase_			:     Optional[Any]									= DataLoader(__UpperCamelCase     ,shuffle=__UpperCamelCase     ,batch_size=__UpperCamelCase     ,num_workers=4 )
		return (train_dataloader, valid_dataloader)


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[Any]=None ):
		lowerCAmelCase_			:     Union[str, Any]									= []
		for epoch in range(__UpperCamelCase ):
				# Train quickly
				model.train()
				for batch in dataloader:
						lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= batch
						lowerCAmelCase_			:     Dict									= model(__UpperCamelCase )
						lowerCAmelCase_			:     Any									= torch.nn.functional.mse_loss(__UpperCamelCase     ,__UpperCamelCase )
						accelerator.backward(__UpperCamelCase )
						optimizer.step()
						optimizer.zero_grad()
				rands.append(random.random() )  # Introduce some randomness
				if scheduler is not None:
						scheduler.step()
		return rands



class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       Union[str, Any]):
									super().__init__()
									lowerCAmelCase_			:     str									= nn.Parameter(torch.randn(1))
									lowerCAmelCase_			:     Dict									= nn.Parameter(torch.randn(1))







							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       int):
									return x * self.a + self.b





class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(4_2)
											lowerCAmelCase_			:     List[str]									= DummyModel()
											lowerCAmelCase_			:     Optional[Any]									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= dummy_dataloaders()
											lowerCAmelCase_			:     int									= ProjectConfiguration(total_limit=1				,				project_dir=A_				,				automatic_checkpoint_naming=A_)
											# Train baseline
											lowerCAmelCase_			:     str									= Accelerator(project_config=A_)
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Union[str, Any]									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_)
											# Save initial
											accelerator.save_state()

											# Save second state
											accelerator.save_state()
											self.assertEqual(len(os.listdir(accelerator.project_dir))				,				1)




							def    UpperCAmelCase__			(       self     :       Tuple):
									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(4_2)
											lowerCAmelCase_			:     Dict									= DummyModel()
											lowerCAmelCase_			:     Any									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= dummy_dataloaders()
											# Train baseline
											lowerCAmelCase_			:     Optional[int]									= Accelerator()
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_)
											# Save initial
											lowerCAmelCase_			:     Union[str, Any]									= os.path.join(A_				,				'''initial''')
											accelerator.save_state(A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     Union[str, Any]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     List[Any]									= optimizer.state_dict()
											lowerCAmelCase_			:     List[Any]									= train(3				,				A_				,				A_				,				A_				,				A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     str									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Tuple									= optimizer.state_dict()

											# Train partially
											set_seed(4_2)
											lowerCAmelCase_			:     List[Any]									= DummyModel()
											lowerCAmelCase_			:     Union[str, Any]									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_					,  lowerCAmelCase_			:     str									= dummy_dataloaders()
											lowerCAmelCase_			:     Dict									= Accelerator()
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_)
											accelerator.load_state(A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     List[Any]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Tuple									= optimizer.state_dict()
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)

											lowerCAmelCase_			:     Union[str, Any]									= train(2				,				A_				,				A_				,				A_				,				A_)
											# Save everything
											lowerCAmelCase_			:     List[str]									= os.path.join(A_				,				'''checkpoint''')
											accelerator.save_state(A_)

											# Load everything back in and make sure all states work
											accelerator.load_state(A_)
											test_rands += train(1				,				A_				,				A_				,				A_				,				A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     List[str]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Union[str, Any]									= optimizer.state_dict()
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)




							def    UpperCAmelCase__			(       self     :       Any):
									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(4_2)
											lowerCAmelCase_			:     Optional[Any]									= DummyModel()
											lowerCAmelCase_			:     List[Any]									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= dummy_dataloaders()
											lowerCAmelCase_			:     Optional[Any]									= ProjectConfiguration(automatic_checkpoint_naming=A_)

											# Train baseline
											lowerCAmelCase_			:     Any									= Accelerator(project_dir=A_				,				project_config=A_)
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     str									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_)
											# Save initial
											accelerator.save_state()
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     Optional[int]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Optional[int]									= optimizer.state_dict()
											lowerCAmelCase_			:     Optional[int]									= train(3				,				A_				,				A_				,				A_				,				A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     Union[str, Any]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Union[str, Any]									= optimizer.state_dict()

											# Train partially
											set_seed(4_2)
											lowerCAmelCase_			:     Any									= DummyModel()
											lowerCAmelCase_			:     str									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= dummy_dataloaders()
											lowerCAmelCase_			:     List[Any]									= ProjectConfiguration(iteration=1				,				automatic_checkpoint_naming=A_)
											lowerCAmelCase_			:     List[str]									= Accelerator(project_dir=A_				,				project_config=A_)
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_)
											accelerator.load_state(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_0'''))
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     Optional[int]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Any									= optimizer.state_dict()
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)

											lowerCAmelCase_			:     Optional[Any]									= train(2				,				A_				,				A_				,				A_				,				A_)
											# Save everything
											accelerator.save_state()

											# Load everything back in and make sure all states work
											accelerator.load_state(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_1'''))
											test_rands += train(1				,				A_				,				A_				,				A_				,				A_)
											((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     List[str]									= model.a.item(), model.b.item()
											lowerCAmelCase_			:     Union[str, Any]									= optimizer.state_dict()
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)
											self.assertEqual(A_				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Union[str, Any]									= torch.tensor([1, 2, 3])
									lowerCAmelCase_			:     Any									= torch.tensor([2, 3, 4])
									lowerCAmelCase_			:     Any									= DummyModel()
									lowerCAmelCase_			:     Dict									= torch.optim.Adam(net.parameters())
									lowerCAmelCase_			:     str									= Accelerator()
									with self.assertRaises(A_) as ve:
											accelerator.register_for_checkpointing(A_				,				A_				,				A_				,				A_)
									lowerCAmelCase_			:     str									= str(ve.exception)
									self.assertTrue('''Item at index 0''' in message)
									self.assertTrue('''Item at index 1''' in message)
									self.assertFalse('''Item at index 2''' in message)
									self.assertFalse('''Item at index 3''' in message)




							def    UpperCAmelCase__			(       self     :       Any):
									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(4_2)
											lowerCAmelCase_			:     Optional[Any]									= DummyModel()
											lowerCAmelCase_			:     str									= torch.optim.Adam(params=model.parameters()				,				lr=1e-3)
											lowerCAmelCase_			:     Optional[int]									= torch.optim.lr_scheduler.StepLR(A_				,				step_size=1				,				gamma=0.99)
											lowerCAmelCase_					,  lowerCAmelCase_			:     str									= dummy_dataloaders()
											lowerCAmelCase_			:     List[str]									= ProjectConfiguration(automatic_checkpoint_naming=A_)
											# Train baseline
											lowerCAmelCase_			:     Optional[int]									= Accelerator(project_dir=A_				,				project_config=A_)
											lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= accelerator.prepare(
											    A_				,				A_				,				A_				,				A_				,				A_)
											# Save initial
											accelerator.save_state()
											lowerCAmelCase_			:     Union[str, Any]									= scheduler.state_dict()
											train(3				,				A_				,				A_				,				A_				,				A_				,				A_)
											self.assertNotEqual(A_				,				scheduler.state_dict())

											# Load everything back in and make sure all states work
											accelerator.load_state(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_0'''))
											self.assertEqual(A_				,				scheduler.state_dict())




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(4_2)
											lowerCAmelCase_			:     List[str]									= DummyModel()
											lowerCAmelCase_			:     Any									= ProjectConfiguration(automatic_checkpoint_naming=A_				,				total_limit=2)
											# Train baseline
											lowerCAmelCase_			:     Optional[int]									= Accelerator(project_dir=A_				,				project_config=A_)
											lowerCAmelCase_			:     List[Any]									= accelerator.prepare(A_)
											# Save 3 states:
											for _ in range(1_1):
													accelerator.save_state()
											self.assertTrue(not os.path.exists(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_0''')))
											self.assertTrue(os.path.exists(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_9''')))
											self.assertTrue(os.path.exists(os.path.join(A_				,				'''checkpoints'''				,				'''checkpoint_10''')))







							@require_cuda
							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     Optional[Any]									= ['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__)]
									execute_subprocess_async(A_				,				env=os.environ.copy())


if __name__ == "__main__":
						A__  :  Optional[int]								=					'''/tmp/accelerate/state_checkpointing'''
						A__  :  List[str]								=					DummyModel()
						A__  :  Optional[Any]								=					torch.optim.Adam(params=model.parameters(), lr=1E-3)
						A__  :  List[str]								=					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
						A__					,     A__  :  Optional[Any]								=					dummy_dataloaders()
						A__  :  Any								=					ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						A__  :  List[str]								=					Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
						if accelerator.process_index == 0:
												if os.path.exists(savedir):
																		shutil.rmtree(savedir)
												os.makedirs(savedir)
						A__					,     A__					,     A__					,     A__					,     A__  :  Any								=					accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						A__					,     A__  :  Dict								=					accelerator.prepare(model, optimizer)
						train(3, model, train_dataloader, optimizer, accelerator, scheduler)
						# Check that the intial optimizer is loaded on the GPU
						for group in optimizer.param_groups:
												A__  :  List[str]								=					group['''params'''][0].device
												break
						assert param_device.type == accelerator.device.type
						A__  :  Optional[Any]								=					model.cpu()
						accelerator.wait_for_everyone()
						accelerator.save_state()
						accelerator.wait_for_everyone()

						# Check CPU state
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
						for group in optimizer.param_groups:
												A__  :  List[str]								=					group['''params'''][0].device
												break
						assert (
						    param_device.type == torch.device('''cpu''').type
						), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

						# Check device state
						model.to(accelerator.device)
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
						for group in optimizer.param_groups:
												A__  :  Optional[int]								=					group['''params'''][0].device
												break
						assert (
						    param_device.type == accelerator.device.type
						), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

						# Check error
						with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
												accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
						accelerator.wait_for_everyone()
						if accelerator.process_index == 0:
												shutil.rmtree(savedir)
						accelerator.wait_for_everyone()










import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





