

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

  return model, optimizer, scheduler, train_dl, valid_dl

def       a__					(		UpperCAmelCase							:      Optional[Any]      )  ->						Optional[Any]:
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def       a__					(		UpperCAmelCase							:      int      )  ->						int:
  UpperCAmelCase	:							Tuple						=			torch.nn.Linear(*tuple(model.weight.T.shape      )      ).state_dict()
  model.load_state_dict(UpperCAmelCase      )






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @require_cuda
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Dict						=			Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(__A						):
       UpperCAmelCase	:							int						=			Accelerator(cpu=__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							List[str]						=			Accelerator()
     UpperCAmelCase	:							Any						=			GradientState()
     assert state.num_steps == 1
     UpperCAmelCase	:							List[str]						=			4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     UpperCAmelCase	:							int						=			False
     assert state.sync_gradients is False
     GradientState._reset_state()



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							Dict						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			create_components()

     (
         (
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						
     )	:							List[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 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Tuple						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			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 	__magic_name__   (							self							:					Union[str, Any]						):
     PartialState._reset_state()

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

     with patch('''torch.cuda.set_device''',		__A						), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''						):
       UpperCAmelCase	:							Optional[int]						=			Accelerator()
       self.assertEqual(str(accelerator.state.device						),		'''cuda:64'''						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Any						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			create_components()
     accelerator.prepare(__A,		__A,		__A,		__A,		__A						)

     UpperCAmelCase	:							List[str]						=			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 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			create_components()
     accelerator.prepare(__A,		__A,		__A,		__A,		__A						)

     UpperCAmelCase	:							int						=			get_signature(__A						)

     # saving hook
     def save_config(__A							:					Optional[int],		__A							:					Optional[int],		__A							:					Optional[Any]						):
       UpperCAmelCase	:							str						=			{'''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							:					Optional[Any],		__A							:					Any						):
       with open(os.path.join(__A,		'''data.json'''						),		'''r'''						) as f:
         UpperCAmelCase	:							Union[str, Any]						=			json.load(__A						)

       UpperCAmelCase	:							Any						=			config['''class_name''']

     UpperCAmelCase	:							str						=			accelerator.register_save_state_pre_hook(__A						)
     UpperCAmelCase	:							Dict						=			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
       UpperCAmelCase	:							Union[str, Any]						=			'''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
       UpperCAmelCase	:							List[str]						=			'''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 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Optional[Any]						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			create_components()
     UpperCAmelCase	:							Dict						=			None

     # This should work
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			accelerator.prepare(
         __A,		__A,		__A,		__A,		__A,		__A						)
     self.assertTrue(dummy_obj is None						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Optional[int]						=			Accelerator()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			create_components()
     UpperCAmelCase	:							Optional[int]						=			[1, 2, 3]

     # This should work
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			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 	__magic_name__   (							self							:					List[str]						):
     from transformers import AutoModelForCausalLM

     UpperCAmelCase	:							Optional[Any]						=			AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''',		load_in_abit=__A,		device_map={'''''': 0},		)
     UpperCAmelCase	:							List[Any]						=			Accelerator()

     # This should work
     UpperCAmelCase	:							List[str]						=			accelerator.prepare(__A						)



   @slow
   @require_bnb
   def 	__magic_name__   (							self							:					int						):
     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	:							List[str]						=			infer_auto_device_map(__A						)
       UpperCAmelCase	:							Tuple						=			'''cpu'''

     UpperCAmelCase	:							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						):
       UpperCAmelCase	:							Union[str, Any]						=			accelerator.prepare(__A						)



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

     UpperCAmelCase	:							Optional[int]						=			{'''distributed_type''': DistributedType.MULTI_GPU}

     with init_empty_weights():
       UpperCAmelCase	:							Dict						=			AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m''',		)
       model.tie_weights()
       UpperCAmelCase	:							int						=			infer_auto_device_map(__A						)
       UpperCAmelCase	:							int						=			1

     UpperCAmelCase	:							Any						=			AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''',		load_in_abit=__A,		device_map=__A,		)
     UpperCAmelCase	:							Optional[Any]						=			Accelerator()

     # This should not work and get value error
     with self.assertRaises(__A						):
       UpperCAmelCase	:							List[str]						=			accelerator.prepare(__A						)

     PartialState._reset_state()



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

     with init_empty_weights():
       UpperCAmelCase	:							int						=			AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m''',		)
       UpperCAmelCase	:							Dict						=			infer_auto_device_map(__A						)
       UpperCAmelCase	:							Any						=			1

     UpperCAmelCase	:							List[str]						=			AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''',		load_in_abit=__A,		device_map=__A,		)
     UpperCAmelCase	:							List[Any]						=			Accelerator()

     # This should work
     UpperCAmelCase	:							List[str]						=			accelerator.prepare(__A						)





   @require_cuda
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							str						=			torch.nn.Linear(1_0,		1_0						)
     UpperCAmelCase	:							Optional[int]						=			torch.optim.SGD(model.parameters(),		lr=0.0_1						)
     UpperCAmelCase	:							Tuple						=			Accelerator(cpu=__A						)
     UpperCAmelCase	:							int						=			accelerator.prepare(__A						)








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





