





'''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  _lowerCamelCase				( )	->	Union[str, Any]:
				_a								= torch.nn.Linear(2    ,							4						)
				_a								= torch.optim.AdamW(model.parameters()    ,							lr=1.0						)
				_a								= torch.optim.lr_scheduler.OneCycleLR(_A    ,							max_lr=0.01    ,							steps_per_epoch=2    ,							epochs=1						)
				_a								= DataLoader(TensorDataset(torch.tensor([1, 2, 3]						)						)						)
				_a								= DataLoader(TensorDataset(torch.tensor([4, 5, 6]						)						)						)

				return model, optimizer, scheduler, train_dl, valid_dl






def  _lowerCamelCase				( lowercase						:      Any						)	->	Any:
				return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def  _lowerCamelCase				( lowercase						:      str						)	->	Any:
				_a								= torch.nn.Linear(*tuple(model.weight.T.shape						)						).state_dict()
				model.load_state_dict(_A						)



class 				__SCREAMING_SNAKE_CASE  (lowerCAmelCase__      ):






				"""simple docstring"""







				@require_cuda
				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= Accelerator()
								assert PartialState._shared_state["_cpu"] is False
								assert PartialState._shared_state["device"].type == "cuda"
								with self.assertRaises(__lowerCAmelCase					):
												_a								= Accelerator(cpu=__lowerCAmelCase					)




				def   UpperCamelCase__   (  self     :					int					):
								_a								= Accelerator()
								_a								= GradientState()
								assert state.num_steps == 1
								_a								= 4
								assert state.num_steps == 4

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




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()

								(
								    (
								    _a     
								)	,					(
								    _a     
								)	,					(
								    _a     
								)	,					(
								    _a     
								)	,					(
								    _a     
								)	,					     
								)								= accelerator.prepare(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					)

								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   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()
								accelerator.prepare(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					)
								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   UpperCamelCase__   (  self     :					Any					):
								PartialState._reset_state()

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

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




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()
								accelerator.prepare(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					)

								_a								= get_signature(__lowerCAmelCase					)

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

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

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()
								accelerator.prepare(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					)

								_a								= get_signature(__lowerCAmelCase					)

								# saving hook
								def save_config(__a     :					List[str]					,				__a     :					int					,				__a     :					Optional[Any]					):
												_a								= {"class_name": models[0].__class__.__name__}

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

        # loading hook
								def load_config(__a     :					Dict					,				__a     :					str					):
												with open(os.path.join(__lowerCAmelCase					,				"data.json"					)					,				"r"					) as f:
																_a								= json.load(__lowerCAmelCase					)

												_a								= config["class_name"]

								_a								= accelerator.register_save_state_pre_hook(__lowerCAmelCase					)
								_a								= accelerator.register_load_state_pre_hook(__lowerCAmelCase					)

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

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

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

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

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

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

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

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




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()
								_a								= None

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




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= Accelerator()
								_a	,					_a	,					_a	,					_a	,					_a								= create_components()
								_a								= [1, 2, 3]

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




				@slow
				@require_bnb
				def   UpperCamelCase__   (  self     :					List[Any]					):
								from transformers import AutoModelForCausalLM

								_a								= AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"					,				load_in_abit=__lowerCAmelCase					,				device_map={"": 0}					,				)
								_a								= Accelerator()

								# This should work
								_a								= accelerator.prepare(__lowerCAmelCase					)




				@slow
				@require_bnb
				def   UpperCamelCase__   (  self     :					str					):
								from transformers import AutoModelForCausalLM

								_a								= Accelerator()

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

								_a								= AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"					,				device_map=__lowerCAmelCase					,				load_in_abit=__lowerCAmelCase					,				llm_inta_enable_fpaa_cpu_offload=__lowerCAmelCase					)

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




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

								_a								= {"distributed_type": DistributedType.MULTI_GPU}

								with init_empty_weights():
												_a								= AutoModelForCausalLM.from_pretrained(
												    "EleutherAI/gpt-neo-125m"					,				)
												model.tie_weights()
												_a								= infer_auto_device_map(__lowerCAmelCase					)
												_a								= 1

								_a								= AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"					,				load_in_abit=__lowerCAmelCase					,				device_map=__lowerCAmelCase					,				)
								_a								= Accelerator()

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

								PartialState._reset_state()




				@slow
				@require_bnb
				@require_multi_gpu
				def   UpperCamelCase__   (  self     :					List[Any]					):
								from transformers import AutoModelForCausalLM

								with init_empty_weights():
												_a								= AutoModelForCausalLM.from_pretrained(
												    "EleutherAI/gpt-neo-125m"					,				)
												_a								= infer_auto_device_map(__lowerCAmelCase					)
												_a								= 1

								_a								= AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"					,				load_in_abit=__lowerCAmelCase					,				device_map=__lowerCAmelCase					,				)
								_a								= Accelerator()

								# This should work
								_a								= accelerator.prepare(__lowerCAmelCase					)




				@require_cuda
				def   UpperCamelCase__   (  self     :					Any					):
								_a								= torch.nn.Linear(10					,				10					)
								_a								= torch.optim.SGD(model.parameters()					,				lr=0.01					)
								_a								= Accelerator(cpu=__lowerCAmelCase					)
								_a								= accelerator.prepare(__lowerCAmelCase					)

'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase






def 				snake_case__							(			_A:					str )    ->	str:

		'''simple docstring'''


		if not sentence:
				return ""
		lowerCAmelCase   					=  dict(zip(_A      ,   _A ) )
		return lower_to_upper.get(sentence[0]      ,   sentence[0] ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




