





"""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		(	) ->List[Any]:






	'''simple docstring'''






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

	return model, optimizer, scheduler, train_dl, valid_dl






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->int:






	'''simple docstring'''






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]					) ->List[Any]:






	'''simple docstring'''






	a      :		Optional[int]	=				torch.nn.Linear(*tuple(model.weight.T.shape					)					).state_dict()
	model.load_state_dict(_lowercase					)







class        __UpperCamelCase				(						a__						):






							@require_cuda
							def     __a   (   self      )	-> int:
								a      :		Union[str, Any]	=				Accelerator()
								assert PartialState._shared_state["_cpu"] is False
								assert PartialState._shared_state["device"].type == "cuda"
								with self.assertRaises(lowerCAmelCase__      ):
									a      :		List[Any]	=				Accelerator(cpu=lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[int]:
								a      :		Optional[int]	=				Accelerator()
								a      :		str	=				GradientState()
								assert state.num_steps == 1
								a      :		str	=				4
								assert state.num_steps == 4

								assert state.sync_gradients is True
								a      :		Optional[Any]	=				False
								assert state.sync_gradients is False
								GradientState._reset_state()






							def     __a   (   self      )	-> List[Any]:
								a      :		Any	=				Accelerator()
								a,       a,       a,       a,       a      :		Tuple	=				create_components()

								(
								    (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       
								)      :		str	=				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     __a   (   self      )	-> int:
								a      :		Optional[int]	=				Accelerator()
								a,       a,       a,       a,       a      :		Any	=				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     __a   (   self      )	-> Tuple:
								PartialState._reset_state()

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

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






							def     __a   (   self      )	-> Any:
								a      :		Dict	=				Accelerator()
								a,       a,       a,       a,       a      :		str	=				create_components()
								accelerator.prepare(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )

								a      :		Optional[Any]	=				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     __a   (   self      )	-> List[str]:
								a      :		Union[str, Any]	=				Accelerator()
								a,       a,       a,       a,       a      :		Union[str, Any]	=				create_components()
								accelerator.prepare(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )

								a      :		Optional[int]	=				get_signature(lowerCAmelCase__      )

								# saving hook
								def save_config(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				{"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(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									with open(os.path.join(lowerCAmelCase__      ,					"data.json"      )      ,					"r"      ) as f:
										a      :		Optional[Any]	=				json.load(lowerCAmelCase__      )

									a      :		str	=				config["class_name"]

								a      :		List[str]	=				accelerator.register_save_state_pre_hook(lowerCAmelCase__      )
								a      :		int	=				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      :		Union[str, Any]	=				"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      :		List[str]	=				"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     __a   (   self      )	-> List[str]:
								a      :		List[str]	=				Accelerator()
								a,       a,       a,       a,       a      :		List[str]	=				create_components()
								a      :		List[Any]	=				None

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






							def     __a   (   self      )	-> List[Any]:
								a      :		Union[str, Any]	=				Accelerator()
								a,       a,       a,       a,       a      :		Union[str, Any]	=				create_components()
								a      :		List[Any]	=				[1, 2, 3]

								# This should work
								a,       a,       a,       a,       a,       a      :		List[str]	=				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     __a   (   self      )	-> Optional[Any]:
								from transformers import AutoModelForCausalLM

								a      :		Union[str, Any]	=				AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"      ,					load_in_abit=lowerCAmelCase__      ,					device_map={"": 0}      ,					)
								a      :		Tuple	=				Accelerator()

								# This should work
								a      :		Dict	=				accelerator.prepare(lowerCAmelCase__      )






							@slow
							@require_bnb
							def     __a   (   self      )	-> Any:
								from transformers import AutoModelForCausalLM

								a      :		Dict	=				Accelerator()

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

								a      :		Union[str, Any]	=				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      :		int	=				accelerator.prepare(lowerCAmelCase__      )






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

								a      :		str	=				{"distributed_type": DistributedType.MULTI_GPU}

								with init_empty_weights():
									a      :		int	=				AutoModelForCausalLM.from_pretrained(
									    "EleutherAI/gpt-neo-125m"      ,					)
									model.tie_weights()
									a      :		List[str]	=				infer_auto_device_map(lowerCAmelCase__      )
									a      :		int	=				1

								a      :		List[Any]	=				AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"      ,					load_in_abit=lowerCAmelCase__      ,					device_map=lowerCAmelCase__      ,					)
								a      :		str	=				Accelerator()

								# This should not work and get value error
								with self.assertRaises(lowerCAmelCase__      ):
									a      :		List[Any]	=				accelerator.prepare(lowerCAmelCase__      )

								PartialState._reset_state()






							@slow
							@require_bnb
							@require_multi_gpu
							def     __a   (   self      )	-> Union[str, Any]:
								from transformers import AutoModelForCausalLM

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

								a      :		List[str]	=				AutoModelForCausalLM.from_pretrained(
								    "EleutherAI/gpt-neo-125m"      ,					load_in_abit=lowerCAmelCase__      ,					device_map=lowerCAmelCase__      ,					)
								a      :		Any	=				Accelerator()

								# This should work
								a      :		Optional[Any]	=				accelerator.prepare(lowerCAmelCase__      )






							@require_cuda
							def     __a   (   self      )	-> str:
								a      :		str	=				torch.nn.Linear(10      ,					10      )
								a      :		str	=				torch.optim.SGD(model.parameters()      ,					lr=0.01      )
								a      :		Optional[Any]	=				Accelerator(cpu=lowerCAmelCase__      )
								a      :		Any	=				accelerator.prepare(lowerCAmelCase__      )









"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


