


"""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      lowercase			(				)->      int:
  '''simple docstring'''







  a						:		Dict      							=			torch.nn.Linear(2  ,   4       )
  a						:		Dict      							=			torch.optim.AdamW(model.parameters()  ,   lr=1.0       )
  a						:		Any      							=			torch.optim.lr_scheduler.OneCycleLR(A__  ,   max_lr=0.0_1  ,   steps_per_epoch=2  ,   epochs=1       )
  a						:		Tuple      							=			DataLoader(TensorDataset(torch.tensor([1, 2, 3]       )       )       )
  a						:		int      							=			DataLoader(TensorDataset(torch.tensor([4, 5, 6]       )       )       )

  return model, optimizer, scheduler, train_dl, valid_dl


def      lowercase			(				A_       )->      Optional[Any]:
  '''simple docstring'''







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


def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  a						:		List[Any]      							=			torch.nn.Linear(*tuple(model.weight.T.shape       )       ).state_dict()
  model.load_state_dict(A__       )







class 						_A  (  __UpperCAmelCase  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Tuple):
      a						:		Tuple      							=			Accelerator()
      a						:		Optional[Any]      							=			GradientState()
      assert state.num_steps == 1
      a						:		Union[str, Any]      							=			4
      assert state.num_steps == 4

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






    def 	__snake_case							(					self				:    List[str]):
      a						:		Tuple      							=			Accelerator()
      a						:		Tuple      							=			create_components()

      (
          a							
      )						:		List[Any]      							=			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 	__snake_case							(					self				:    Optional[int]):
      a						:		Union[str, Any]      							=			Accelerator()
      a						:		List[str]      							=			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 	__snake_case							(					self				:    str):
      PartialState._reset_state()

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

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






    def 	__snake_case							(					self				:    str):
      a						:		Optional[int]      							=			Accelerator()
      a						:		Union[str, Any]      							=			create_components()
      accelerator.prepare(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)

      a						:		Tuple      							=			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 	__snake_case							(					self				:    Optional[int]):
      a						:		List[str]      							=			Accelerator()
      a						:		Optional[int]      							=			create_components()
      accelerator.prepare(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)

      a						:		List[str]      							=			get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase				:    str				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Any):
        a						:		int      							=			{'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				:    str				, __UpperCAmelCase				:    Tuple):
        with open(os.path.join(__UpperCAmelCase				, "data.json")				, "r") as f:
          a						:		List[Any]      							=			json.load(__UpperCAmelCase)

        a						:		Union[str, Any]      							=			config['class_name']

      a						:		Optional[Any]      							=			accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		str      							=			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						:		int      							=			'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						:		Union[str, Any]      							=			'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 	__snake_case							(					self				:    List[str]):
      a						:		str      							=			Accelerator()
      a						:		str      							=			create_components()
      a						:		Dict      							=			None

      # This should work
      a						:		Tuple      							=			accelerator.prepare(
          __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






    def 	__snake_case							(					self				:    int):
      a						:		List[Any]      							=			Accelerator()
      a						:		int      							=			create_components()
      a						:		int      							=			[1, 2, 3]

      # This should work
      a						:		Any      							=			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 	__snake_case							(					self				:    Dict):
      from transformers import AutoModelForCausalLM

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

      # This should work
      a						:		Union[str, Any]      							=			accelerator.prepare(__UpperCAmelCase)






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

      a						:		Dict      							=			Accelerator()

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

      a						:		str      							=			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						:		List[str]      							=			accelerator.prepare(__UpperCAmelCase)






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

      a						:		int      							=			{'distributed_type': DistributedType.MULTI_GPU}

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

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

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

      PartialState._reset_state()






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

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

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

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






    @require_cuda
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Optional[int]      							=			torch.nn.Linear(10				, 10)
      a						:		Optional[int]      							=			torch.optim.SGD(model.parameters()				, lr=0.01)
      a						:		Optional[int]      							=			Accelerator(cpu=__UpperCAmelCase)
      a						:		str      							=			accelerator.prepare(__UpperCAmelCase)













def A_	(		A__		,      A__				)  ->		str:
       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'				)

       a__						:	List[str] =				str(bin(A__				)				)[2:]  # remove the leading "0b"
       a__						:	Optional[int] =				str(bin(A__				)				)[2:]  # remove the leading "0b"

       a__						:	List[str] =				max(len(A__				)		,      len(A__				)				)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'				)				)
           for char_a, char_b in zip(a_binary.zfill(A__				)		,      b_binary.zfill(A__				)				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


