
'''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      UpperCamelCase_				(      ):
	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      UpperCamelCase_				(      A__						):
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def      UpperCamelCase_				(      A__						):
	a_      				=						torch.nn.Linear(*tuple(model.weight.T.shape						)						).state_dict()
	model.load_state_dict(A__						)







class   a_ (      UpperCamelCase__					):




			@require_cuda
			def 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(UpperCAmelCase	):
					a_      				=						Accelerator(cpu=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				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 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()

				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						accelerator.prepare(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

				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 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()
				accelerator.prepare(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
				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 					lowerCAmelCase__					(       self	):
				PartialState._reset_state()

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

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()
				accelerator.prepare(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

				a_      				=						get_signature(UpperCAmelCase	)

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

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

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()
				accelerator.prepare(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

				a_      				=						get_signature(UpperCAmelCase	)

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

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

        # loading hook
				def load_config(UpperCAmelCase					,   UpperCAmelCase	):
					with open(os.path.join(UpperCAmelCase					,   """data.json"""	)					,   """r"""	) as f:
						a_      				=						json.load(UpperCAmelCase	)

					a_      				=						config["""class_name"""]

				a_      				=						accelerator.register_save_state_pre_hook(UpperCAmelCase	)
				a_      				=						accelerator.register_load_state_pre_hook(UpperCAmelCase	)

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

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

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

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

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

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

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

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()
				a_      				=						None

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Accelerator()
				a_				,  a_				,  a_				,  a_				,  a_      				=						create_components()
				a_      				=						[1, 2, 3]

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




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

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

				# This should work
				a_      				=						accelerator.prepare(UpperCAmelCase	)




			@slow
			@require_bnb
			def 					lowerCAmelCase__					(       self	):
				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(UpperCAmelCase	)
					a_      				=						"""cpu"""

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

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




			@slow
			@require_bnb
			@require_multi_gpu
			def 					lowerCAmelCase__					(       self	):
				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(UpperCAmelCase	)
					a_      				=						1

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

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

				PartialState._reset_state()




			@slow
			@require_bnb
			@require_multi_gpu
			def 					lowerCAmelCase__					(       self	):
				from transformers import AutoModelForCausalLM

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

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

				# This should work
				a_      				=						accelerator.prepare(UpperCAmelCase	)






			@require_cuda
			def 					lowerCAmelCase__					(       self	):
				a_      				=						torch.nn.Linear(10					,   10	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.01	)
				a_      				=						Accelerator(cpu=UpperCAmelCase	)
				a_      				=						accelerator.prepare(UpperCAmelCase	)


'''simple docstring'''
import math






def      UpperCamelCase_				(      A__ = 1_00						):
	a_      				=						sum(i * i for i in range(1  ,      n + 1						)						)
	a_      				=						int(math.pow(sum(range(1  ,      n + 1						)						)  ,      2						)						)
	return square_of_sum - sum_of_squares


if __name__ == "__main__":
		print(F"""{solution() = }""")
