






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							( )			->     Dict:
					UpperCamelCase__      :       Any	    =  torch.nn.Linear(2 ,   4      )
					UpperCamelCase__      :       Tuple	    =  torch.optim.AdamW(model.parameters() ,   lr=1.0      )
					UpperCamelCase__      :       Union[str, Any]	    =  torch.optim.lr_scheduler.OneCycleLR(_lowerCAmelCase ,   max_lr=0.0_1 ,   steps_per_epoch=2 ,   epochs=1      )
					UpperCamelCase__      :       str	    =  DataLoader(TensorDataset(torch.tensor([1, 2, 3]      )      )      )
					UpperCamelCase__      :       List[str]	    =  DataLoader(TensorDataset(torch.tensor([4, 5, 6]      )      )      )

					return model, optimizer, scheduler, train_dl, valid_dl







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     List[Any]:
					return (model.weight.abs().sum() + model.bias.abs().sum()).item()







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[Any]:
					UpperCamelCase__      :       List[str]	    =  torch.nn.Linear(*tuple(model.weight.T.shape      )      ).state_dict()
					model.load_state_dict(_lowerCAmelCase      )





class       __a  (   A__	):







	@require_cuda
	def  __lowercase (		self :       Optional[Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       Optional[int]	    =  Accelerator()
						assert PartialState._shared_state["_cpu"] is False
						assert PartialState._shared_state["device"].type == "cuda"
						with self.assertRaises(_lowerCAmelCase						):
											UpperCamelCase__      :       Tuple	    =  Accelerator(cpu=_lowerCAmelCase						)







	def  __lowercase (		self :       Tuple						):


						'''simple docstring'''






						UpperCamelCase__      :       Optional[int]	    =  Accelerator()
						UpperCamelCase__      :       Optional[Any]	    =  GradientState()
						assert state.num_steps == 1
						UpperCamelCase__      :       Optional[Any]	    =  4
						assert state.num_steps == 4

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







	def  __lowercase (		self :       List[str]						):


						'''simple docstring'''






						UpperCamelCase__      :       str	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  create_components()

						(
						    (
						    UpperCamelCase__			
						)    ,					(
						    UpperCamelCase__			
						)    ,					(
						    UpperCamelCase__			
						)    ,					(
						    UpperCamelCase__			
						)    ,					(
						    UpperCamelCase__			
						)    ,								
						)      :       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  __lowercase (		self :       Optional[int]						):


						'''simple docstring'''






						UpperCamelCase__      :       Tuple	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Optional[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  __lowercase (		self :       Any						):


						'''simple docstring'''






						PartialState._reset_state()

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

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







	def  __lowercase (		self :       int						):


						'''simple docstring'''






						UpperCamelCase__      :       int	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       int	    =  create_components()
						accelerator.prepare(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase						)

						UpperCamelCase__      :       Dict	    =  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  __lowercase (		self :       Tuple						):


						'''simple docstring'''






						UpperCamelCase__      :       List[str]	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  create_components()
						accelerator.prepare(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase						)

						UpperCamelCase__      :       Tuple	    =  get_signature(_lowerCAmelCase						)

						# saving hook
						def save_config(SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Optional[int]						):
											UpperCamelCase__      :       int	    =  {"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(SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):
											with open(os.path.join(_lowerCAmelCase							,       "data.json"						)							,       "r"						) as f:
																UpperCamelCase__      :       Union[str, Any]	    =  json.load(_lowerCAmelCase						)

											UpperCamelCase__      :       Optional[int]	    =  config["class_name"]

						UpperCamelCase__      :       int	    =  accelerator.register_save_state_pre_hook(_lowerCAmelCase						)
						UpperCamelCase__      :       List[Any]	    =  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
											UpperCamelCase__      :       Optional[int]	    =  "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
											UpperCamelCase__      :       Tuple	    =  "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  __lowercase (		self :       List[str]						):


						'''simple docstring'''






						UpperCamelCase__      :       str	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  create_components()
						UpperCamelCase__      :       List[Any]	    =  None

						# This should work
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  accelerator.prepare(
						    _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase						)
						self.assertTrue(dummy_obj is None						)







	def  __lowercase (		self :       int						):


						'''simple docstring'''






						UpperCamelCase__      :       List[Any]	    =  Accelerator()
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  create_components()
						UpperCamelCase__      :       Optional[int]	    =  [1, 2, 3]

						# This should work
						UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       List[Any]	    =  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  __lowercase (		self :       Union[str, Any]						):


						'''simple docstring'''






						from transformers import AutoModelForCausalLM

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

						# This should work
						UpperCamelCase__      :       Any	    =  accelerator.prepare(_lowerCAmelCase						)







	@slow
	@require_bnb
	def  __lowercase (		self :       Union[str, Any]						):


						'''simple docstring'''






						from transformers import AutoModelForCausalLM

						UpperCamelCase__      :       Dict	    =  Accelerator()

						with init_empty_weights():
											UpperCamelCase__      :       List[Any]	    =  AutoModelForCausalLM.from_pretrained(
											    "EleutherAI/gpt-neo-125m"							,       )
											model.tie_weights()
											UpperCamelCase__      :       str	    =  infer_auto_device_map(_lowerCAmelCase						)
											UpperCamelCase__      :       str	    =  "cpu"

						UpperCamelCase__      :       Tuple	    =  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						):
											UpperCamelCase__      :       str	    =  accelerator.prepare(_lowerCAmelCase						)







	@slow
	@require_bnb
	@require_multi_gpu
	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						from transformers import AutoModelForCausalLM

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

						with init_empty_weights():
											UpperCamelCase__      :       List[str]	    =  AutoModelForCausalLM.from_pretrained(
											    "EleutherAI/gpt-neo-125m"							,       )
											model.tie_weights()
											UpperCamelCase__      :       Optional[Any]	    =  infer_auto_device_map(_lowerCAmelCase						)
											UpperCamelCase__      :       Union[str, Any]	    =  1

						UpperCamelCase__      :       Any	    =  AutoModelForCausalLM.from_pretrained(
						    "EleutherAI/gpt-neo-125m"							,       load_in_abit=_lowerCAmelCase							,       device_map=_lowerCAmelCase							,       )
						UpperCamelCase__      :       Tuple	    =  Accelerator()

						# This should not work and get value error
						with self.assertRaises(_lowerCAmelCase						):
											UpperCamelCase__      :       int	    =  accelerator.prepare(_lowerCAmelCase						)

						PartialState._reset_state()







	@slow
	@require_bnb
	@require_multi_gpu
	def  __lowercase (		self :       Optional[Any]						):


						'''simple docstring'''






						from transformers import AutoModelForCausalLM

						with init_empty_weights():
											UpperCamelCase__      :       int	    =  AutoModelForCausalLM.from_pretrained(
											    "EleutherAI/gpt-neo-125m"							,       )
											UpperCamelCase__      :       Dict	    =  infer_auto_device_map(_lowerCAmelCase						)
											UpperCamelCase__      :       List[Any]	    =  1

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

						# This should work
						UpperCamelCase__      :       int	    =  accelerator.prepare(_lowerCAmelCase						)







	@require_cuda
	def  __lowercase (		self :       Optional[Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       Any	    =  torch.nn.Linear(10							,       10						)
						UpperCamelCase__      :       Optional[int]	    =  torch.optim.SGD(model.parameters()							,       lr=0.0_1						)
						UpperCamelCase__      :       Any	    =  Accelerator(cpu=_lowerCAmelCase						)
						UpperCamelCase__      :       List[Any]	    =  accelerator.prepare(_lowerCAmelCase						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     assert isinstance(__lowerCAmelCase ,   __lowerCAmelCase      ), f'The input value of [n={number}] is not an integer'

     if number == 1:
          return 2
     elif number < 1:
          UpperCamelCase__      :       List[Any]	    =  f'The input value of [n={number}] has to be > 0'
          raise ValueError(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Optional[Any]	    =  sylvester(number - 1      )
          UpperCamelCase__      :       str	    =  num - 1
          UpperCamelCase__      :       int	    =  num
          return lower * upper + 1


if __name__ == "__main__":
 print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")