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

       return model, optimizer, scheduler, train_dl, valid_dl






def 					snake_case    (			snake_case__     :Tuple)		->  Optional[Any]:
       return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 					snake_case    (			snake_case__     :Union[str, Any])		->  Union[str, Any]:
       _A	  =    torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
       model.load_state_dict(a__)







class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 @require_cuda
 def 	UpperCAmelCase				(   self				)    ->   str:
        _A	  =    Accelerator()
        assert PartialState._shared_state["_cpu"] is False
        assert PartialState._shared_state["device"].type == "cuda"
        with self.assertRaises(__SCREAMING_SNAKE_CASE				):
               _A	  =    Accelerator(cpu=__SCREAMING_SNAKE_CASE				)



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    Accelerator()
        _A	  =    GradientState()
        assert state.num_steps == 1
        _A	  =    4
        assert state.num_steps == 4

        assert state.sync_gradients is True
        _A	  =    False
        assert state.sync_gradients is False
        GradientState._reset_state()



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()

        (
            (
            _A     
        )  ,    (
            _A     
        )  ,    (
            _A     
        )  ,    (
            _A     
        )  ,    (
            _A     
        )  ,         
        )	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)

        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 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()
        accelerator.prepare(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)
        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 	UpperCAmelCase				(   self				)    ->   Optional[int]:
        PartialState._reset_state()

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

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



 def 	UpperCAmelCase				(   self				)    ->   str:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()
        accelerator.prepare(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)

        _A	  =    get_signature(__SCREAMING_SNAKE_CASE				)

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

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

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



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()
        accelerator.prepare(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)

        _A	  =    get_signature(__SCREAMING_SNAKE_CASE				)

        # saving hook
        def save_config(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				):
               _A	  =    {"""class_name""": models[0].__class__.__name__}

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

        # loading hook
        def load_config(lowerCAmelCase_						,							lowerCAmelCase_				):
               with open(os.path.join(__SCREAMING_SNAKE_CASE						,							"""data.json"""				)						,							"""r"""				) as f:
                      _A	  =    json.load(__SCREAMING_SNAKE_CASE				)

               _A	  =    config["""class_name"""]

        _A	  =    accelerator.register_save_state_pre_hook(__SCREAMING_SNAKE_CASE				)
        _A	  =    accelerator.register_load_state_pre_hook(__SCREAMING_SNAKE_CASE				)

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

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

               # random class name to verify correct one is loaded
               _A	  =    """random"""

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

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

               # random class name to verify correct one is loaded
               _A	  =    """random"""

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

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



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()
        _A	  =    None

        # This should work
        _A  ,    _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    accelerator.prepare(
            __SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)
        self.assertTrue(dummy_obj is None				)



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    Accelerator()
        _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    create_components()
        _A	  =    [1, 2, 3]

        # This should work
        _A  ,    _A  ,    _A  ,    _A  ,    _A  ,    _A	  =    accelerator.prepare(
            __SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Dummy object should have `_is_accelerate_prepared` set to `True`"""						,							)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Model is missing `_is_accelerator_prepared` or is set to `False`"""						,							)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""						,							)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""						,							)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""						,							)
        self.assertEqual(
            getattr(__SCREAMING_SNAKE_CASE						,							"""_is_accelerate_prepared"""						,							__SCREAMING_SNAKE_CASE				)						,							__SCREAMING_SNAKE_CASE						,							"""Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""						,							)



 @slow
 @require_bnb
 def 	UpperCAmelCase				(   self				)    ->   Dict:
        from transformers import AutoModelForCausalLM

        _A	  =    AutoModelForCausalLM.from_pretrained(
            """EleutherAI/gpt-neo-125m"""						,							load_in_abit=__SCREAMING_SNAKE_CASE						,							device_map={"""""": 0}						,							)
        _A	  =    Accelerator()

        # This should work
        _A	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE				)



 @slow
 @require_bnb
 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        from transformers import AutoModelForCausalLM

        _A	  =    Accelerator()

        with init_empty_weights():
               _A	  =    AutoModelForCausalLM.from_pretrained(
                   """EleutherAI/gpt-neo-125m"""						,							)
               model.tie_weights()
               _A	  =    infer_auto_device_map(__SCREAMING_SNAKE_CASE				)
               _A	  =    """cpu"""

        _A	  =    AutoModelForCausalLM.from_pretrained(
            """EleutherAI/gpt-neo-125m"""						,							device_map=__SCREAMING_SNAKE_CASE						,							load_in_abit=__SCREAMING_SNAKE_CASE						,							llm_inta_enable_fpaa_cpu_offload=__SCREAMING_SNAKE_CASE				)

        # This should not work and get value error
        with self.assertRaises(__SCREAMING_SNAKE_CASE				):
               _A	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE				)



 @slow
 @require_bnb
 @require_multi_gpu
 def 	UpperCAmelCase				(   self				)    ->   int:
        from transformers import AutoModelForCausalLM

        _A	  =    {"""distributed_type""": DistributedType.MULTI_GPU}

        with init_empty_weights():
               _A	  =    AutoModelForCausalLM.from_pretrained(
                   """EleutherAI/gpt-neo-125m"""						,							)
               model.tie_weights()
               _A	  =    infer_auto_device_map(__SCREAMING_SNAKE_CASE				)
               _A	  =    1

        _A	  =    AutoModelForCausalLM.from_pretrained(
            """EleutherAI/gpt-neo-125m"""						,							load_in_abit=__SCREAMING_SNAKE_CASE						,							device_map=__SCREAMING_SNAKE_CASE						,							)
        _A	  =    Accelerator()

        # This should not work and get value error
        with self.assertRaises(__SCREAMING_SNAKE_CASE				):
               _A	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE				)

        PartialState._reset_state()



 @slow
 @require_bnb
 @require_multi_gpu
 def 	UpperCAmelCase				(   self				)    ->   Any:
        from transformers import AutoModelForCausalLM

        with init_empty_weights():
               _A	  =    AutoModelForCausalLM.from_pretrained(
                   """EleutherAI/gpt-neo-125m"""						,							)
               _A	  =    infer_auto_device_map(__SCREAMING_SNAKE_CASE				)
               _A	  =    1

        _A	  =    AutoModelForCausalLM.from_pretrained(
            """EleutherAI/gpt-neo-125m"""						,							load_in_abit=__SCREAMING_SNAKE_CASE						,							device_map=__SCREAMING_SNAKE_CASE						,							)
        _A	  =    Accelerator()

        # This should work
        _A	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE				)



 @require_cuda
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    torch.nn.Linear(10						,							10				)
        _A	  =    torch.optim.SGD(model.parameters()						,							lr=0.01				)
        _A	  =    Accelerator(cpu=__SCREAMING_SNAKE_CASE				)
        _A	  =    accelerator.prepare(__SCREAMING_SNAKE_CASE				)







'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






