






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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[Any]								=						AutoencoderKL
     UpperCamelCase_				:		Any								=						'''sample'''
     UpperCamelCase_				:		int								=						1e-2


     @property
     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     4
       SCREAMING_SNAKE_CASE							:   Tuple						    =     3
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     (32, 32)

       SCREAMING_SNAKE_CASE							:   Dict						    =     floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase_   )

       return {"sample": image}


     @property
     def 					_A							(					self							:						Any   ):
       return (3, 32, 32)


     @property
     def 					_A							(					self							:						str   ):
       return (3, 32, 32)


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {
           "block_out_channels": [32, 64],
           "in_channels": 3,
           "out_channels": 3,
           "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
           "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
           "latent_channels": 4,
       }
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.dummy_input
       return init_dict, inputs_dict


     def 					_A							(					self							:						Optional[int]   ):
       pass


     def 					_A							(					self							:						Union[str, Any]   ):
       pass


     @unittest.skipIf(torch_device == "mps"       ,			"Gradient checkpointing skipped on MPS"   )
     def 					_A							(					self							:						Optional[int]   ):
       # enable deterministic behavior for gradient checkpointing
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.model_class(**UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )

       assert not model.is_gradient_checkpointing and model.training

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(**UpperCAmelCase_   ).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()

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.randn_like(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (out - labels).mean()
       loss.backward()

       # re-instantiate the model now enabling gradient checkpointing
       SCREAMING_SNAKE_CASE							:   Any						    =     self.model_class(**UpperCAmelCase_   )
       # clone model
       model_a.load_state_dict(model.state_dict()   )
       model_a.to(UpperCAmelCase_   )
       model_a.enable_gradient_checkpointing()

       assert model_a.is_gradient_checkpointing and model_a.training

       SCREAMING_SNAKE_CASE							:   List[str]						    =     model_a(**UpperCAmelCase_   ).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()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     (out_a - labels).mean()
       loss_a.backward()

       # compare the output and parameters gradients
       self.assertTrue((loss - loss_a).abs() < 1E-5   )
       SCREAMING_SNAKE_CASE							:   Any						    =     dict(model.named_parameters()   )
       SCREAMING_SNAKE_CASE							:   Any						    =     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							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Dict						    =     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"       ,			output_loading_info=UpperCAmelCase_   )
       self.assertIsNotNone(UpperCAmelCase_   )
       self.assertEqual(len(loading_info["missing_keys"]   )       ,			0   )

       model.to(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     model(**self.dummy_input   )

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






     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model.to(UpperCAmelCase_   )
       model.eval()

       if torch_device == "mps":
         SCREAMING_SNAKE_CASE							:   Any						    =     torch.manual_seed(0   )
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )

       SCREAMING_SNAKE_CASE							:   int						    =     torch.randn(
           1       ,			model.config.in_channels       ,			model.config.sample_size       ,			model.config.sample_size       ,			generator=torch.manual_seed(0   )       ,			)
       SCREAMING_SNAKE_CASE							:   Any						    =     image.to(UpperCAmelCase_   )
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model(UpperCAmelCase_       ,			sample_posterior=UpperCAmelCase_       ,			generator=UpperCAmelCase_   ).sample

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     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":
         SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.tensor(
             [
                 -4.0_078E-01,
                 -3.8_323E-04,
                 -1.2_681E-01,
                 -1.1_462E-01,
                 2.0_095E-01,
                 1.0_893E-01,
                 -8.8_247E-02,
                 -3.0_361E-01,
                 -9.8_644E-03,
             ]   )
       elif torch_device == "cpu":
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.tensor(
             [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]   )
       else:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.tensor(
             [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]   )

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





@slow
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Dict   ):
       return f'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCAmelCase_   ) 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							:						Tuple       ,			UpperCAmelCase_							:						List[Any]=0       ,			UpperCAmelCase_							:						Dict=(4, 3, 512, 512)       ,			UpperCAmelCase_							:						Any=False   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.floataa if fpaa else torch.floataa
       SCREAMING_SNAKE_CASE							:   Dict						    =     torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCAmelCase_       ,			UpperCAmelCase_   )   )   ).to(UpperCAmelCase_   ).to(UpperCAmelCase_   )
       return image


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any="CompVis/stable-diffusion-v1-4"       ,			UpperCAmelCase_							:						List[str]=False   ):
       SCREAMING_SNAKE_CASE							:   int						    =     "fp16" if fpaa else None
       SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.floataa if fpaa else torch.floataa

       SCREAMING_SNAKE_CASE							:   Any						    =     AutoencoderKL.from_pretrained(
           UpperCAmelCase_       ,			subfolder="vae"       ,			torch_dtype=UpperCAmelCase_       ,			revision=UpperCAmelCase_       ,			)
       model.to(UpperCAmelCase_   ).eval()

       return model


     def 					_A							(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						int=0   ):
       if torch_device == "mps":
         return torch.manual_seed(UpperCAmelCase_   )
       return torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )


     @parameterized.expand(
         [
             # fmt: off
             [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
             [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
             # fmt: on
         ]   )
     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_sd_vae_model()
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.get_sd_image(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_generator(UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   List[str]						    =     model(UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			sample_posterior=UpperCAmelCase_   ).sample

       assert sample.shape == image.shape

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     sample[-1, -2:, -2:, :2].flatten().float().cpu()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice   )

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


     @parameterized.expand(
         [
             # fmt: off
             [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
             [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
             # fmt: on
         ]   )
     @require_torch_gpu
     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.get_sd_vae_model(fpaa=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_sd_image(UpperCAmelCase_       ,			fpaa=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   int						    =     self.get_generator(UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			sample_posterior=UpperCAmelCase_   ).sample

       assert sample.shape == image.shape

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
       SCREAMING_SNAKE_CASE							:   str						    =     torch.tensor(UpperCAmelCase_   )

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


     @parameterized.expand(
         [
             # fmt: off
             [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
             [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
             # fmt: on
         ]   )
     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.get_sd_vae_model()
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_sd_image(UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(UpperCAmelCase_   ).sample

       assert sample.shape == image.shape

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

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


     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
             [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
             # fmt: on
         ]   )
     @require_torch_gpu
     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.get_sd_vae_model()
       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_sd_image(UpperCAmelCase_       ,			shape=(3, 4, 64, 64)   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model.decode(UpperCAmelCase_   ).sample

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

       SCREAMING_SNAKE_CASE							:   Dict						    =     sample[-1, -2:, :2, -2:].flatten().cpu()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.tensor(UpperCAmelCase_   )

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


     @parameterized.expand(
         [
             # fmt: off
             [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
             [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
             # fmt: on
         ]   )
     @require_torch_gpu
     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.get_sd_vae_model(fpaa=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_sd_image(UpperCAmelCase_       ,			shape=(3, 4, 64, 64)       ,			fpaa=UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model.decode(UpperCAmelCase_   ).sample

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

       SCREAMING_SNAKE_CASE							:   int						    =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.tensor(UpperCAmelCase_   )

       assert torch_all_close(UpperCAmelCase_       ,			UpperCAmelCase_       ,			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							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.get_sd_vae_model(fpaa=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   int						    =     self.get_sd_image(UpperCAmelCase_       ,			shape=(3, 4, 64, 64)       ,			fpaa=UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     model.decode(UpperCAmelCase_   ).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     model.decode(UpperCAmelCase_   ).sample

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

       assert torch_all_close(UpperCAmelCase_       ,			UpperCAmelCase_       ,			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							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_sd_vae_model()
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.get_sd_image(UpperCAmelCase_       ,			shape=(3, 4, 64, 64)   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Dict						    =     model.decode(UpperCAmelCase_   ).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   int						    =     model.decode(UpperCAmelCase_   ).sample

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

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






     @parameterized.expand(
         [
             # fmt: off
             [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
             [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
             # fmt: on
         ]   )
     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.get_sd_vae_model()
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.get_sd_image(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.get_generator(UpperCAmelCase_   )

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model.encode(UpperCAmelCase_   ).latent_dist
         SCREAMING_SNAKE_CASE							:   List[str]						    =     dist.sample(generator=UpperCAmelCase_   )

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

       SCREAMING_SNAKE_CASE							:   List[str]						    =     sample[0, -1, -3:, -3:].flatten().cpu()
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.tensor(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     3E-3 if torch_device != "mps" else 1E-2
       assert torch_all_close(UpperCAmelCase_       ,			UpperCAmelCase_       ,			atol=UpperCAmelCase_   )














# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)