



'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch

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

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def 		_SCREAMING_SNAKE_CASE				(					):


			'''simple docstring'''







			A:   int								= torch.nn.Linear(2				,				4					)
			A:   Tuple								= torch.optim.AdamW(model.parameters()				,				lr=1.0					)
			A:   Dict								= torch.optim.lr_scheduler.OneCycleLR(__a				,				max_lr=0.01				,				steps_per_epoch=2				,				epochs=1					)
			A:   Tuple								= DataLoader(TensorDataset(torch.tensor([1, 2, 3]					)					)					)
			A:   List[str]								= DataLoader(TensorDataset(torch.tensor([4, 5, 6]					)					)					)

			return model, optimizer, scheduler, train_dl, valid_dl



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Tuple					):


			'''simple docstring'''







			return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Tuple					):


			'''simple docstring'''







			A:   Optional[Any]								= torch.nn.Linear(*tuple(model.weight.T.shape					)					).state_dict()
			model.load_state_dict(__a					)


class 	SCREAMING_SNAKE_CASE__					( _lowercase       ):
	"""simple docstring"""


	@require_cuda
	def   a__			(		self      )			->				str:
				A:   str								= Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(_snake_case      ):
							A:   Tuple								= Accelerator(cpu=_snake_case      )


	def   a__			(		self      )			->				List[str]:
				A:   Any								= Accelerator()
				A:   Any								= GradientState()
				assert state.num_steps == 1
				A:   Optional[int]								= 4
				assert state.num_steps == 4

				assert state.sync_gradients is True
				A:   Union[str, Any]								= False
				assert state.sync_gradients is False
				GradientState._reset_state()


	def   a__			(		self      )			->				Optional[int]:
				A:   str								= Accelerator()
				A				,		A				,		A				,		A				,		A:   Any								= create_components()

				(
				    (
				    A     
				)				,		(
				    A     
				)				,		(
				    A     
				)				,		(
				    A     
				)				,		(
				    A     
				)				,		     
				):   Dict								= accelerator.prepare(_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )

				self.assertTrue(prepared_model in accelerator._models      )
				self.assertTrue(prepared_optimizer in accelerator._optimizers      )
				self.assertTrue(prepared_scheduler in accelerator._schedulers      )
				self.assertTrue(prepared_train_dl in accelerator._dataloaders      )
				self.assertTrue(prepared_valid_dl in accelerator._dataloaders      )


	def   a__			(		self      )			->				Optional[Any]:
				A:   Dict								= Accelerator()
				A				,		A				,		A				,		A				,		A:   Tuple								= create_components()
				accelerator.prepare(_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )
				accelerator.free_memory()

				self.assertTrue(len(accelerator._models      ) == 0      )
				self.assertTrue(len(accelerator._optimizers      ) == 0      )
				self.assertTrue(len(accelerator._schedulers      ) == 0      )
				self.assertTrue(len(accelerator._dataloaders      ) == 0      )


	def   a__			(		self      )			->				List[Any]:
				PartialState._reset_state()

				# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
				def noop(*A       , **A      ):
							pass

				with patch("""torch.cuda.set_device"""       , _snake_case      ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""      ):
							A:   Dict								= Accelerator()
							self.assertEqual(str(accelerator.state.device      )       , """cuda:64"""      )


	def   a__			(		self      )			->				Any:
				A:   Union[str, Any]								= Accelerator()
				A				,		A				,		A				,		A				,		A:   Optional[int]								= create_components()
				accelerator.prepare(_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )

				A:   Union[str, Any]								= get_signature(_snake_case      )

				with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(_snake_case      )

							# make sure random weights don't match
							load_random_weights(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) > 1e-3      )

							# make sure loaded weights match
							accelerator.load_state(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) < 1e-3      )


	def   a__			(		self      )			->				str:
				A:   Optional[Any]								= Accelerator()
				A				,		A				,		A				,		A				,		A:   Optional[Any]								= create_components()
				accelerator.prepare(_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )

				A:   Dict								= get_signature(_snake_case      )

				# saving hook
				def save_config(A       , A       , A      ):
							A:   Optional[Any]								= {"""class_name""": models[0].__class__.__name__}

							with open(os.path.join(_snake_case       , """data.json"""      )       , """w"""      ) as f:
										json.dump(_snake_case       , _snake_case      )

        # loading hook
				def load_config(A       , A      ):
							with open(os.path.join(_snake_case       , """data.json"""      )       , """r"""      ) as f:
										A:   List[str]								= json.load(_snake_case      )

							A:   int								= config["""class_name"""]

				A:   Any								= accelerator.register_save_state_pre_hook(_snake_case      )
				A:   List[Any]								= accelerator.register_load_state_pre_hook(_snake_case      )

				with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(_snake_case      )

							# make sure random weights don't match with hooks
							load_random_weights(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) > 1e-3      )

							# random class name to verify correct one is loaded
							A:   Tuple								= """random"""

							# make sure loaded weights match with hooks
							accelerator.load_state(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) < 1e-3      )

							# mode.class_name is loaded from config
							self.assertTrue(model.class_name == model.__class__.__name__      )

				# remove hooks
				save_hook.remove()
				load_hook.remove()

				with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(_snake_case      )

							# make sure random weights don't match with hooks removed
							load_random_weights(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) > 1e-3      )

							# random class name to verify correct one is loaded
							A:   List[Any]								= """random"""

							# make sure loaded weights match with hooks removed
							accelerator.load_state(_snake_case      )
							self.assertTrue(abs(model_signature - get_signature(_snake_case      )      ) < 1e-3      )

							# mode.class_name is NOT loaded from config
							self.assertTrue(model.class_name != model.__class__.__name__      )


	def   a__			(		self      )			->				Optional[Any]:
				A:   List[str]								= Accelerator()
				A				,		A				,		A				,		A				,		A:   int								= create_components()
				A:   Optional[int]								= None

				# This should work
				A				,		A				,		A				,		A				,		A				,		A:   Optional[Any]								= accelerator.prepare(
				    _snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )
				self.assertTrue(dummy_obj is None      )


	def   a__			(		self      )			->				List[str]:
				A:   Tuple								= Accelerator()
				A				,		A				,		A				,		A				,		A:   Optional[int]								= create_components()
				A:   Optional[int]								= [1, 2, 3]

				# This should work
				A				,		A				,		A				,		A				,		A				,		A:   Tuple								= accelerator.prepare(
				    _snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case      )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Dummy object should have `_is_accelerate_prepared` set to `True`"""       , )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Model is missing `_is_accelerator_prepared` or is set to `False`"""       , )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""       , )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""       , )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""       , )
				self.assertEqual(
				    getattr(_snake_case       , """_is_accelerate_prepared"""       , _snake_case      )       , _snake_case       , """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""       , )


	@slow
	@require_bnb
	def   a__			(		self      )			->				str:
				from transformers import AutoModelForCausalLM

				A:   Dict								= AutoModelForCausalLM.from_pretrained(
				    """EleutherAI/gpt-neo-125m"""       , load_in_abit=_snake_case       , device_map={"""""": 0}       , )
				A:   List[str]								= Accelerator()

				# This should work
				A:   Tuple								= accelerator.prepare(_snake_case      )


	@slow
	@require_bnb
	def   a__			(		self      )			->				Optional[Any]:
				from transformers import AutoModelForCausalLM

				A:   str								= Accelerator()

				with init_empty_weights():
							A:   Optional[Any]								= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       , )
							model.tie_weights()
							A:   int								= infer_auto_device_map(_snake_case      )
							A:   Any								= """cpu"""

				A:   Tuple								= AutoModelForCausalLM.from_pretrained(
				    """EleutherAI/gpt-neo-125m"""       , device_map=_snake_case       , load_in_abit=_snake_case       , llm_inta_enable_fpaa_cpu_offload=_snake_case      )

				# This should not work and get value error
				with self.assertRaises(_snake_case      ):
							A:   Tuple								= accelerator.prepare(_snake_case      )


	@slow
	@require_bnb
	@require_multi_gpu
	def   a__			(		self      )			->				Optional[int]:
				from transformers import AutoModelForCausalLM

				A:   Optional[int]								= {"""distributed_type""": DistributedType.MULTI_GPU}

				with init_empty_weights():
							A:   str								= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       , )
							model.tie_weights()
							A:   Optional[Any]								= infer_auto_device_map(_snake_case      )
							A:   int								= 1

				A:   Dict								= AutoModelForCausalLM.from_pretrained(
				    """EleutherAI/gpt-neo-125m"""       , load_in_abit=_snake_case       , device_map=_snake_case       , )
				A:   Tuple								= Accelerator()

				# This should not work and get value error
				with self.assertRaises(_snake_case      ):
							A:   Any								= accelerator.prepare(_snake_case      )

				PartialState._reset_state()


	@slow
	@require_bnb
	@require_multi_gpu
	def   a__			(		self      )			->				Optional[Any]:
				from transformers import AutoModelForCausalLM

				with init_empty_weights():
							A:   Optional[Any]								= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""       , )
							A:   List[str]								= infer_auto_device_map(_snake_case      )
							A:   int								= 1

				A:   int								= AutoModelForCausalLM.from_pretrained(
				    """EleutherAI/gpt-neo-125m"""       , load_in_abit=_snake_case       , device_map=_snake_case       , )
				A:   str								= Accelerator()

				# This should work
				A:   Union[str, Any]								= accelerator.prepare(_snake_case      )





	@require_cuda
	def   a__			(		self      )			->				str:
				A:   List[str]								= torch.nn.Linear(10       , 10      )
				A:   Union[str, Any]								= torch.optim.SGD(model.parameters()       , lr=0.01      )
				A:   Dict								= Accelerator(cpu=_snake_case      )
				A:   Any								= accelerator.prepare(_snake_case      )







"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




