






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"""






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

  return model, optimizer, scheduler, train_dl, valid_dl


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






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


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @require_cuda
     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     Accelerator(cpu=UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     Accelerator()
       SCREAMING_SNAKE_CASE							:   int						    =     GradientState()
       assert state.num_steps == 1
       SCREAMING_SNAKE_CASE							:   int						    =     4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       SCREAMING_SNAKE_CASE							:   Tuple						    =     False
       assert state.sync_gradients is False
       GradientState._reset_state()


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     create_components()

       (
           (
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	
       )							:   Tuple						    =     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 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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 					_A							(					self							:						Any   ):
       PartialState._reset_state()

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

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


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     create_components()
       accelerator.prepare(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   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 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     create_components()
       accelerator.prepare(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   str						    =     get_signature(UpperCAmelCase_   )

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

         SCREAMING_SNAKE_CASE							:   List[str]						    =     config["class_name"]

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     accelerator.register_save_state_pre_hook(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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
         SCREAMING_SNAKE_CASE							:   Optional[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
         SCREAMING_SNAKE_CASE							:   Tuple						    =     "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 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     create_components()
       SCREAMING_SNAKE_CASE							:   int						    =     None

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


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     Accelerator()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     create_components()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [1, 2, 3]

       # This should work
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[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 					_A							(					self							:						Dict   ):
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE							:   int						    =     AutoModelForCausalLM.from_pretrained(
           "EleutherAI/gpt-neo-125m"       ,			load_in_abit=UpperCAmelCase_       ,			device_map={"": 0}       ,			)
       SCREAMING_SNAKE_CASE							:   str						    =     Accelerator()

       # This should work
       SCREAMING_SNAKE_CASE							:   List[str]						    =     accelerator.prepare(UpperCAmelCase_   )


     @slow
     @require_bnb
     def 					_A							(					self							:						Tuple   ):
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE							:   Tuple						    =     Accelerator()

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

       SCREAMING_SNAKE_CASE							:   Dict						    =     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_   ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     accelerator.prepare(UpperCAmelCase_   )


     @slow
     @require_bnb
     @require_multi_gpu
     def 					_A							(					self							:						int   ):
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE							:   str						    =     {"distributed_type": DistributedType.MULTI_GPU}

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

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

       # This should not work and get value error
       with self.assertRaises(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   str						    =     accelerator.prepare(UpperCAmelCase_   )

       PartialState._reset_state()


     @slow
     @require_bnb
     @require_multi_gpu
     def 					_A							(					self							:						Dict   ):
       from transformers import AutoModelForCausalLM

       with init_empty_weights():
         SCREAMING_SNAKE_CASE							:   int						    =     AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,			)
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     infer_auto_device_map(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Any						    =     1

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

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






     @require_cuda
     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.nn.Linear(10       ,			10   )
       SCREAMING_SNAKE_CASE							:   str						    =     torch.optim.SGD(model.parameters()       ,			lr=0.01   )
       SCREAMING_SNAKE_CASE							:   str						    =     Accelerator(cpu=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     accelerator.prepare(UpperCAmelCase_   )








"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


