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        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







			lowercase							=		torch.nn.Linear(2,		4       )
			lowercase							=		torch.optim.AdamW(model.parameters(),		lr=1.0       )
			lowercase							=		torch.optim.lr_scheduler.OneCycleLR(_lowerCAmelCase,		max_lr=0.01,		steps_per_epoch=2,		epochs=1       )
			lowercase							=		DataLoader(TensorDataset(torch.tensor([1, 2, 3]       )       )       )
			lowercase							=		DataLoader(TensorDataset(torch.tensor([4, 5, 6]       )       )       )

			return model, optimizer, scheduler, train_dl, valid_dl






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		torch.nn.Linear(*tuple(model.weight.T.shape       )       ).state_dict()
			model.load_state_dict(_lowerCAmelCase       )






class      __lowercase					(    __snake_case    ):

						@require_cuda
						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		Accelerator()
									assert PartialState._shared_state["_cpu"] is False
									assert PartialState._shared_state["device"].type == "cuda"
									with self.assertRaises(A_   ):
												lowercase							=		Accelerator(cpu=A_   )

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		GradientState()
									assert state.num_steps == 1
									lowercase							=		4
									assert state.num_steps == 4

									assert state.sync_gradients is True
									lowercase							=		False
									assert state.sync_gradients is False
									GradientState._reset_state()

						def        __a		(	self				:      Optional[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()

									(
									    lowercase					
									)							=		accelerator.prepare(A_							,							A_							,							A_							,							A_							,							A_   )

									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				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()
									accelerator.prepare(A_							,							A_							,							A_							,							A_							,							A_   )
									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				:      Optional[int]   )							->		int:


									'''simple docstring'''

									PartialState._reset_state()

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

									with patch('''torch.cuda.set_device'''							,							A_   ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''   ):
												lowercase							=		Accelerator()
												self.assertEqual(str(accelerator.state.device   )							,							'''cuda:64'''   )

						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()
									accelerator.prepare(A_							,							A_							,							A_							,							A_							,							A_   )

									lowercase							=		get_signature(A_   )

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

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

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

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()
									accelerator.prepare(A_							,							A_							,							A_							,							A_							,							A_   )

									lowercase							=		get_signature(A_   )

									# saving hook
									def save_config(__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[int]   ):
												lowercase							=		{"class_name": models[0].__class__.__name__}

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

												# loading hook
									def load_config(__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      str   ):
												with open(os.path.join(A_							,							'''data.json'''   )							,							'''r'''   ) as f:
															lowercase							=		json.load(A_   )

												lowercase							=		config["class_name"]

									lowercase							=		accelerator.register_save_state_pre_hook(A_   )
									lowercase							=		accelerator.register_load_state_pre_hook(A_   )

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

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

												# random class name to verify correct one is loaded
												lowercase							=		"random"

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

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

												# random class name to verify correct one is loaded
												lowercase							=		"random"

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

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

						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()
									lowercase							=		None

									# This should work
									lowercase							=		accelerator.prepare(
									    A_							,							A_							,							A_							,							A_							,							A_							,							A_   )
									self.assertTrue(dummy_obj is None   )

						def        __a		(	self				:      Optional[Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		Accelerator()
									lowercase							=		create_components()
									lowercase							=		[1, 2, 3]

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

						@slow
						@require_bnb
						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									from transformers import AutoModelForCausalLM

									lowercase							=		AutoModelForCausalLM.from_pretrained(
									    '''EleutherAI/gpt-neo-125m'''							,							load_in_abit=A_							,							device_map={'''''': 0}							,							)
									lowercase							=		Accelerator()

									# This should work
									lowercase							=		accelerator.prepare(A_   )

						@slow
						@require_bnb
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									from transformers import AutoModelForCausalLM

									lowercase							=		Accelerator()

									with init_empty_weights():
												lowercase							=		AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''							,							)
												model.tie_weights()
												lowercase							=		infer_auto_device_map(A_   )
												lowercase							=		"cpu"

									lowercase							=		AutoModelForCausalLM.from_pretrained(
									    '''EleutherAI/gpt-neo-125m'''							,							device_map=A_							,							load_in_abit=A_							,							llm_inta_enable_fpaa_cpu_offload=A_   )

									# This should not work and get value error
									with self.assertRaises(A_   ):
												lowercase							=		accelerator.prepare(A_   )

						@slow
						@require_bnb
						@require_multi_gpu
						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									from transformers import AutoModelForCausalLM

									lowercase							=		{"distributed_type": DistributedType.MULTI_GPU}

									with init_empty_weights():
												lowercase							=		AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''							,							)
												model.tie_weights()
												lowercase							=		infer_auto_device_map(A_   )
												lowercase							=		1

									lowercase							=		AutoModelForCausalLM.from_pretrained(
									    '''EleutherAI/gpt-neo-125m'''							,							load_in_abit=A_							,							device_map=A_							,							)
									lowercase							=		Accelerator()

									# This should not work and get value error
									with self.assertRaises(A_   ):
												lowercase							=		accelerator.prepare(A_   )

									PartialState._reset_state()

						@slow
						@require_bnb
						@require_multi_gpu
						def        __a		(	self				:      str   )							->		Dict:


									'''simple docstring'''

									from transformers import AutoModelForCausalLM

									with init_empty_weights():
												lowercase							=		AutoModelForCausalLM.from_pretrained(
												    '''EleutherAI/gpt-neo-125m'''							,							)
												lowercase							=		infer_auto_device_map(A_   )
												lowercase							=		1

									lowercase							=		AutoModelForCausalLM.from_pretrained(
									    '''EleutherAI/gpt-neo-125m'''							,							load_in_abit=A_							,							device_map=A_							,							)
									lowercase							=		Accelerator()

									# This should work
									lowercase							=		accelerator.prepare(A_   )



						@require_cuda
						def        __a		(	self				:      Union[str, Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		torch.nn.Linear(10							,							10   )
									lowercase							=		torch.optim.SGD(model.parameters()							,							lr=0.01   )
									lowercase							=		Accelerator(cpu=A_   )
									lowercase							=		accelerator.prepare(A_   )


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

						def __repr__(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									return self.version_str






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





