


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_ (       ):
       '''simple docstring'''





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

       return model, optimizer, scheduler, train_dl, valid_dl




def       a_ (       SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





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




def       a_ (       SCREAMING_SNAKE_CASE__   : List[str]	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
       model.load_state_dict(SCREAMING_SNAKE_CASE__	)




class A    (  UpperCamelCase_	):






      @require_cuda
      def 			lowerCamelCase			(   self		:	Any					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =Accelerator()
             assert PartialState._shared_state["_cpu"] is False
             assert PartialState._shared_state["device"].type == "cuda"
             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						List[Any]            =Accelerator(cpu=lowercase_					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =Accelerator()
             _lowerCamelCase			:						Tuple            =GradientState()
             assert state.num_steps == 1
             _lowerCamelCase			:						Tuple            =4
             assert state.num_steps == 4

             assert state.sync_gradients is True
             _lowerCamelCase			:						Dict            =False
             assert state.sync_gradients is False
             GradientState._reset_state()






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =Accelerator()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =create_components()

             (
                 (
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	
             )			:						Optional[Any]            =accelerator.prepare(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)

             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		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =Accelerator()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Dict            =create_components()
             accelerator.prepare(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)
             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		:	Union[str, Any]					)   ->		Optional[int]:
             """simple docstring"""




             PartialState._reset_state()

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

             with patch('torch.cuda.set_device'     ,  lowercase_					), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'					):
                    _lowerCamelCase			:						Union[str, Any]            =Accelerator()
                    self.assertEqual(str(accelerator.state.device					)     ,  'cuda:64'					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						int            =Accelerator()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =create_components()
             accelerator.prepare(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =get_signature(lowercase_					)

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

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

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






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =Accelerator()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Dict            =create_components()
             accelerator.prepare(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)

             _lowerCamelCase			:						List[Any]            =get_signature(lowercase_					)

             # saving hook
             def save_config(lowercase_		:	Optional[Any]     ,  lowercase_		:	str     ,  lowercase_		:	int					):
                    _lowerCamelCase			:						int            ={'class_name': models[0].__class__.__name__}

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

        # loading hook
             def load_config(lowercase_		:	Union[str, Any]     ,  lowercase_		:	Tuple					):
                    with open(os.path.join(lowercase_     ,  'data.json'					)     ,  'r'					) as f:
                           _lowerCamelCase			:						Any            =json.load(lowercase_					)

                    _lowerCamelCase			:						Optional[int]            =config['class_name']

             _lowerCamelCase			:						Any            =accelerator.register_save_state_pre_hook(lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =accelerator.register_load_state_pre_hook(lowercase_					)

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase			:						Tuple            ='random'

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase			:						Optional[Any]            ='random'

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

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






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




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

             # This should work
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						int            =accelerator.prepare(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)
             self.assertTrue(dummy_obj is None					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =Accelerator()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Dict            =create_components()
             _lowerCamelCase			:						List[Any]            =[1, 2, 3]

             # This should work
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =accelerator.prepare(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Dummy object should have `_is_accelerate_prepared` set to `True`'     ,  )
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Model is missing `_is_accelerator_prepared` or is set to `False`'     ,  )
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'     ,  )
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'     ,  )
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'     ,  )
             self.assertEqual(
                 getattr(lowercase_     ,  '_is_accelerate_prepared'     ,  lowercase_					)     ,  lowercase_     ,  'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'     ,  )






      @slow
      @require_bnb
      def 			lowerCamelCase			(   self		:	Any					)   ->		List[Any]:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             _lowerCamelCase			:						int            =AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'     ,  load_in_abit=lowercase_     ,  device_map={'': 0}     ,  )
             _lowerCamelCase			:						Dict            =Accelerator()

             # This should work
             _lowerCamelCase			:						str            =accelerator.prepare(lowercase_					)






      @slow
      @require_bnb
      def 			lowerCamelCase			(   self		:	int					)   ->		int:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             _lowerCamelCase			:						List[Any]            =Accelerator()

             with init_empty_weights():
                    _lowerCamelCase			:						Union[str, Any]            =AutoModelForCausalLM.from_pretrained(
                        'EleutherAI/gpt-neo-125m'     ,  )
                    model.tie_weights()
                    _lowerCamelCase			:						Optional[int]            =infer_auto_device_map(lowercase_					)
                    _lowerCamelCase			:						str            ='cpu'

             _lowerCamelCase			:						List[Any]            =AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'     ,  device_map=lowercase_     ,  load_in_abit=lowercase_     ,  llm_inta_enable_fpaa_cpu_offload=lowercase_					)

             # This should not work and get value error
             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Optional[int]            =accelerator.prepare(lowercase_					)






      @slow
      @require_bnb
      @require_multi_gpu
      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[str]:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             _lowerCamelCase			:						str            ={'distributed_type': DistributedType.MULTI_GPU}

             with init_empty_weights():
                    _lowerCamelCase			:						Optional[Any]            =AutoModelForCausalLM.from_pretrained(
                        'EleutherAI/gpt-neo-125m'     ,  )
                    model.tie_weights()
                    _lowerCamelCase			:						Any            =infer_auto_device_map(lowercase_					)
                    _lowerCamelCase			:						Dict            =1

             _lowerCamelCase			:						List[str]            =AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'     ,  load_in_abit=lowercase_     ,  device_map=lowercase_     ,  )
             _lowerCamelCase			:						int            =Accelerator()

             # This should not work and get value error
             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Optional[Any]            =accelerator.prepare(lowercase_					)

             PartialState._reset_state()






      @slow
      @require_bnb
      @require_multi_gpu
      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		int:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             with init_empty_weights():
                    _lowerCamelCase			:						str            =AutoModelForCausalLM.from_pretrained(
                        'EleutherAI/gpt-neo-125m'     ,  )
                    _lowerCamelCase			:						Union[str, Any]            =infer_auto_device_map(lowercase_					)
                    _lowerCamelCase			:						List[str]            =1

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

             # This should work
             _lowerCamelCase			:						Optional[int]            =accelerator.prepare(lowercase_					)






      @require_cuda
      def 			lowerCamelCase			(   self		:	int					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Any            =torch.nn.Linear(10     ,  10					)
             _lowerCamelCase			:						Optional[Any]            =torch.optim.SGD(model.parameters()     ,  lr=0.01					)
             _lowerCamelCase			:						Optional[int]            =Accelerator(cpu=lowercase_					)
             _lowerCamelCase			:						Dict            =accelerator.prepare(lowercase_					)






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	List[Any]					)   ->		None:
             """simple docstring"""




             warnings.warn(
                 'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileViTImageProcessor instead.'     ,  lowercase_     ,  )
             super().__init__(*lowercase_     ,  **lowercase_					)


