


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class    UpperCAmelCase__	( A_       ,  A_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  AutoencoderKL
    UpperCAmelCase__						:		Tuple              =  "sample"
    UpperCAmelCase__						:		Optional[int]              =  1e-2






    @property
    def _a (      self   )  ->	int:
         __UpperCamelCase   							=4
         __UpperCamelCase   							=3
         __UpperCamelCase   							=(32, 32)

         __UpperCamelCase   							=floats_tensor((batch_size, num_channels) + sizes   ).to(A_   )

         return {"sample": image}






    @property
    def _a (      self   )  ->	Any:
         return (3, 32, 32)






    @property
    def _a (      self   )  ->	Union[str, Any]:
         return (3, 32, 32)






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase   							={
             'block_out_channels': [32, 64],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 4,
         }
         __UpperCamelCase   							=self.dummy_input
         return init_dict, inputs_dict






    def _a (      self   )  ->	List[Any]:
         pass






    def _a (      self   )  ->	Union[str, Any]:
         pass






    @unittest.skipIf(torch_device == 'mps' ,       'Gradient checkpointing skipped on MPS'   )
    def _a (      self   )  ->	str:
         # enable deterministic behavior for gradient checkpointing
         __UpperCamelCase							,				__UpperCamelCase   							=self.prepare_init_args_and_inputs_for_common()
         __UpperCamelCase   							=self.model_class(**A_   )
         model.to(A_   )

         assert not model.is_gradient_checkpointing and model.training

         __UpperCamelCase   							=model(**A_   ).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model.zero_grad()

         __UpperCamelCase   							=torch.randn_like(A_   )
         __UpperCamelCase   							=(out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         __UpperCamelCase   							=self.model_class(**A_   )
         # clone model
         model_a.load_state_dict(model.state_dict()   )
         model_a.to(A_   )
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         __UpperCamelCase   							=model_a(**A_   ).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model_a.zero_grad()
         __UpperCamelCase   							=(out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1E-5   )
         __UpperCamelCase   							=dict(model.named_parameters()   )
         __UpperCamelCase   							=dict(model_a.named_parameters()   )
         for name, param in named_params.items():
              self.assertTrue(torch_all_close(param.grad.data ,       named_params_a[name].grad.data ,       atol=5E-5   )   )






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase							,				__UpperCamelCase   							=AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' ,       output_loading_info=A_   )
         self.assertIsNotNone(A_   )
         self.assertEqual(len(loading_info['missing_keys']   ) ,       0   )

         model.to(A_   )
         __UpperCamelCase   							=model(**self.dummy_input   )

         assert image is not None, "Make sure output is not None"






    def _a (      self   )  ->	str:
         __UpperCamelCase   							=AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'   )
         __UpperCamelCase   							=model.to(A_   )
         model.eval()

         if torch_device == "mps":
              __UpperCamelCase   							=torch.manual_seed(0   )
         else:
              __UpperCamelCase   							=torch.Generator(device=A_   ).manual_seed(0   )

         __UpperCamelCase   							=torch.randn(
             1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0   ) ,       )
         __UpperCamelCase   							=image.to(A_   )
         with torch.no_grad():
              __UpperCamelCase   							=model(A_ ,       sample_posterior=A_ ,       generator=A_   ).sample

         __UpperCamelCase   							=output[0, -1, -3:, -3:].flatten().cpu()

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         if torch_device == "mps":
              __UpperCamelCase   							=torch.tensor(
                  [
                      -4.0078E-01,
                      -3.8323E-04,
                      -1.2681E-01,
                      -1.1462E-01,
                      2.0095E-01,
                      1.0893E-01,
                      -8.8247E-02,
                      -3.0361E-01,
                      -9.8644E-03,
                  ]   )
         elif torch_device == "cpu":
              __UpperCamelCase   							=torch.tensor(
                  [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]   )
         else:
              __UpperCamelCase   							=torch.tensor(
                  [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]   )

         self.assertTrue(torch_all_close(A_ ,       A_ ,       rtol=1E-2   )   )






@slow
class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(A_   ) for s in shape]   )}.npy'






    def _a (      self   )  ->	Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def _a (      self ,       A_=0 ,       A_=(4, 3, 512, 512) ,       A_=False   )  ->	List[str]:
         __UpperCamelCase   							=torch.floataa if fpaa else torch.floataa
         __UpperCamelCase   							=torch.from_numpy(load_hf_numpy(self.get_file_format(A_ ,       A_   )   )   ).to(A_   ).to(A_   )
         return image






    def _a (      self ,       A_="CompVis/stable-diffusion-v1-4" ,       A_=False   )  ->	Dict:
         __UpperCamelCase   							='fp16' if fpaa else None
         __UpperCamelCase   							=torch.floataa if fpaa else torch.floataa

         __UpperCamelCase   							=AutoencoderKL.from_pretrained(
             A_ ,       subfolder='vae' ,       torch_dtype=A_ ,       revision=A_ ,       )
         model.to(A_   ).eval()

         return model






    def _a (      self ,       A_=0   )  ->	Any:
         if torch_device == "mps":
              return torch.manual_seed(A_   )
         return torch.Generator(device=A_   ).manual_seed(A_   )






    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]   )
    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=self.get_sd_vae_model()
         __UpperCamelCase   							=self.get_sd_image(A_   )
         __UpperCamelCase   							=self.get_generator(A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model(A_ ,       generator=A_ ,       sample_posterior=A_   ).sample

         assert sample.shape == image.shape

         __UpperCamelCase   							=sample[-1, -2:, -2:, :2].flatten().float().cpu()
         __UpperCamelCase   							=torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice   )

         assert torch_all_close(A_ ,       A_ ,       atol=3E-3   )






    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
            # fmt: on
        ]   )
    @require_torch_gpu
    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							=self.get_sd_vae_model(fpaa=A_   )
         __UpperCamelCase   							=self.get_sd_image(A_ ,       fpaa=A_   )
         __UpperCamelCase   							=self.get_generator(A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model(A_ ,       generator=A_ ,       sample_posterior=A_   ).sample

         assert sample.shape == image.shape

         __UpperCamelCase   							=sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __UpperCamelCase   							=torch.tensor(A_   )

         assert torch_all_close(A_ ,       A_ ,       atol=1E-2   )






    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]   )
    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							=self.get_sd_vae_model()
         __UpperCamelCase   							=self.get_sd_image(A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model(A_   ).sample

         assert sample.shape == image.shape

         __UpperCamelCase   							=sample[-1, -2:, -2:, :2].flatten().float().cpu()
         __UpperCamelCase   							=torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice   )

         assert torch_all_close(A_ ,       A_ ,       atol=3E-3   )






    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
            # fmt: on
        ]   )
    @require_torch_gpu
    def _a (      self ,       A_ ,       A_   )  ->	str:
         __UpperCamelCase   							=self.get_sd_vae_model()
         __UpperCamelCase   							=self.get_sd_image(A_ ,       shape=(3, 4, 64, 64)   )

         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         assert list(sample.shape   ) == [3, 3, 512, 512]

         __UpperCamelCase   							=sample[-1, -2:, :2, -2:].flatten().cpu()
         __UpperCamelCase   							=torch.tensor(A_   )

         assert torch_all_close(A_ ,       A_ ,       atol=1E-3   )






    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
            # fmt: on
        ]   )
    @require_torch_gpu
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=self.get_sd_vae_model(fpaa=A_   )
         __UpperCamelCase   							=self.get_sd_image(A_ ,       shape=(3, 4, 64, 64) ,       fpaa=A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         assert list(sample.shape   ) == [3, 3, 512, 512]

         __UpperCamelCase   							=sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __UpperCamelCase   							=torch.tensor(A_   )

         assert torch_all_close(A_ ,       A_ ,       atol=5E-3   )






    @parameterized.expand([(13,), (16,), (27,)]   )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available() ,       reason='xformers is not required when using PyTorch 2.0.'   )
    def _a (      self ,       A_   )  ->	List[Any]:
         __UpperCamelCase   							=self.get_sd_vae_model(fpaa=A_   )
         __UpperCamelCase   							=self.get_sd_image(A_ ,       shape=(3, 4, 64, 64) ,       fpaa=A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         assert list(sample.shape   ) == [3, 3, 512, 512]

         assert torch_all_close(A_ ,       A_ ,       atol=1E-1   )






    @parameterized.expand([(13,), (16,), (37,)]   )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available() ,       reason='xformers is not required when using PyTorch 2.0.'   )
    def _a (      self ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=self.get_sd_vae_model()
         __UpperCamelCase   							=self.get_sd_image(A_ ,       shape=(3, 4, 64, 64)   )

         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
              __UpperCamelCase   							=model.decode(A_   ).sample

         assert list(sample.shape   ) == [3, 3, 512, 512]

         assert torch_all_close(A_ ,       A_ ,       atol=1E-2   )






    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
            # fmt: on
        ]   )
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=self.get_sd_vae_model()
         __UpperCamelCase   							=self.get_sd_image(A_   )
         __UpperCamelCase   							=self.get_generator(A_   )

         with torch.no_grad():
              __UpperCamelCase   							=model.encode(A_   ).latent_dist
              __UpperCamelCase   							=dist.sample(generator=A_   )

         assert list(sample.shape   ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

         __UpperCamelCase   							=sample[0, -1, -3:, -3:].flatten().cpu()
         __UpperCamelCase   							=torch.tensor(A_   )

         __UpperCamelCase   							=3E-3 if torch_device != 'mps' else 1E-2
         assert torch_all_close(A_ ,       A_ ,       atol=A_   )









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs() ,       {}   )
         self.assertDictEqual(MockClass(a=2   ).to_kwargs() ,       {'a': 2}   )
         self.assertDictEqual(MockClass(a=2 ,       b=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         self.assertEqual(scaler._growth_factor ,       2.0   )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor ,       0.5   )
         self.assertEqual(scaler._growth_interval ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





