





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		(  ):
   '''simple docstring'''


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

   return model, optimizer, scheduler, train_dl, valid_dl



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
   model.load_state_dict(_lowerCAmelCase     )







class    _UpperCAmelCase							(		_lowerCamelCase     ):
      @require_cuda
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(a__       ):
            A_     :				List[Any]			      =	Accelerator(cpu=a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	Accelerator()
         A_     :				Union[str, Any]			      =	GradientState()
         assert state.num_steps == 1
         A_     :				str			      =	4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         A_     :				Tuple			      =	False
         assert state.sync_gradients is False
         GradientState._reset_state()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				Union[str, Any]			      =	create_components()

         (
             (
             A_
         )      ,				(
             A_
         )      ,				(
             A_
         )      ,				(
             A_
         )      ,				(
             A_
         )      ,				
         )     :				Union[str, Any]			      =	accelerator.prepare(a__      ,       a__      ,       a__      ,       a__      ,       a__       )

         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_     :				Any			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				int			      =	create_components()
         accelerator.prepare(a__      ,       a__      ,       a__      ,       a__      ,       a__       )
         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(*a__      ,       **a__       ):
            pass

         with patch("""torch.cuda.set_device"""      ,       a__       ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""       ):
            A_     :				Any			      =	Accelerator()
            self.assertEqual(str(accelerator.state.device       )      ,       """cuda:64"""       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				Any			      =	create_components()
         accelerator.prepare(a__      ,       a__      ,       a__      ,       a__      ,       a__       )

         A_     :				Optional[int]			      =	get_signature(a__       )

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

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

            # make sure loaded weights match
            accelerator.load_state(a__       )
            self.assertTrue(abs(model_signature - get_signature(a__       )       ) < 1E-3       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				Optional[int]			      =	create_components()
         accelerator.prepare(a__      ,       a__      ,       a__      ,       a__      ,       a__       )

         A_     :				Union[str, Any]			      =	get_signature(a__       )

         # saving hook
         def save_config(a__      ,       a__      ,       a__       ):
            A_     :				Optional[int]			      =	{"""class_name""": models[0].__class__.__name__}

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

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

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

         A_     :				List[Any]			      =	accelerator.register_save_state_pre_hook(a__       )
         A_     :				Tuple			      =	accelerator.register_load_state_pre_hook(a__       )

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

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

            # random class name to verify correct one is loaded
            A_     :				str			      =	"""random"""

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

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

            # random class name to verify correct one is loaded
            A_     :				Dict			      =	"""random"""

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

            # mode.class_name is NOT loaded from config
            self.assertTrue(model.class_name != model.__class__.__name__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				Optional[Any]			      =	create_components()
         A_     :				Dict			      =	None

         # This should work
         A_      ,				A_      ,				A_      ,				A_      ,				A_      ,				A_     :				str			      =	accelerator.prepare(
             a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__       )
         self.assertTrue(dummy_obj is None       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	Accelerator()
         A_      ,				A_      ,				A_      ,				A_      ,				A_     :				List[Any]			      =	create_components()
         A_     :				Optional[Any]			      =	[1, 2, 3]

         # This should work
         A_      ,				A_      ,				A_      ,				A_      ,				A_      ,				A_     :				List[Any]			      =	accelerator.prepare(
             a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Dummy object should have `_is_accelerate_prepared` set to `True`"""      ,       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Model is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
         self.assertEqual(
             getattr(a__      ,       """_is_accelerate_prepared"""      ,       a__       )      ,       a__      ,       """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
      @slow
      @require_bnb
      def 		_lowerCamelCase      (		self       ):
         from transformers import AutoModelForCausalLM

         A_     :				Optional[int]			      =	AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=a__      ,       device_map={"""""": 0}      ,       )
         A_     :				Optional[Any]			      =	Accelerator()

         # This should work
         A_     :				Dict			      =	accelerator.prepare(a__       )
      @slow
      @require_bnb
      def 		_lowerCamelCase      (		self       ):
         from transformers import AutoModelForCausalLM

         A_     :				str			      =	Accelerator()

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

         A_     :				Dict			      =	AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""      ,       device_map=a__      ,       load_in_abit=a__      ,       llm_inta_enable_fpaa_cpu_offload=a__       )

         # This should not work and get value error
         with self.assertRaises(a__       ):
            A_     :				Optional[int]			      =	accelerator.prepare(a__       )
      @slow
      @require_bnb
      @require_multi_gpu
      def 		_lowerCamelCase      (		self       ):
         from transformers import AutoModelForCausalLM

         A_     :				Tuple			      =	{"""distributed_type""": DistributedType.MULTI_GPU}

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

         A_     :				Dict			      =	AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=a__      ,       device_map=a__      ,       )
         A_     :				Tuple			      =	Accelerator()

         # This should not work and get value error
         with self.assertRaises(a__       ):
            A_     :				Dict			      =	accelerator.prepare(a__       )

         PartialState._reset_state()
      @slow
      @require_bnb
      @require_multi_gpu
      def 		_lowerCamelCase      (		self       ):
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
            A_     :				int			      =	AutoModelForCausalLM.from_pretrained(
                """EleutherAI/gpt-neo-125m"""      ,       )
            A_     :				Any			      =	infer_auto_device_map(a__       )
            A_     :				Dict			      =	1

         A_     :				str			      =	AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=a__      ,       device_map=a__      ,       )
         A_     :				Dict			      =	Accelerator()

         # This should work
         A_     :				Optional[Any]			      =	accelerator.prepare(a__       )






      @require_cuda
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	torch.nn.Linear(10      ,       10       )
         A_     :				Any			      =	torch.optim.SGD(model.parameters()      ,       lr=0.01       )
         A_     :				int			      =	Accelerator(cpu=a__       )
         A_     :				Union[str, Any]			      =	accelerator.prepare(a__       )













from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )






