
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  (		lowercase_							,						lowercase_							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  AutoencoderKL
			__A						  =  '''sample'''
			__A						  =  1E-2


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 4
				snake_case_      		= 3
				snake_case_      		= (32, 32)

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

				return {"sample": image}


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (3, 32, 32)


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (3, 32, 32)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				snake_case_      		= self.dummy_input
				return init_dict, inputs_dict


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skipIf(torch_device == 'mps' ,				'Gradient checkpointing skipped on MPS'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.prepare_init_args_and_inputs_for_common()
				snake_case_      		= self.model_class(**__UpperCamelCase							)
				model.to(__UpperCamelCase							)

				assert not model.is_gradient_checkpointing and model.training

				snake_case_      		= 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()

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

				# re-instantiate the model now enabling gradient checkpointing
				snake_case_      		= 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

				snake_case_      		= 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()
				snake_case_      		= (out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1E-5							)
				snake_case_      		= dict(model.named_parameters()							)
				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= 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							)
				snake_case_      		= model(**self.dummy_input							)

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







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'							)
				snake_case_      		= model.to(__UpperCamelCase							)
				model.eval()

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

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

				snake_case_      		= 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":
					snake_case_      		= torch.tensor(
					    [
					        -4.00_78E-01,
					        -3.83_23E-04,
					        -1.26_81E-01,
					        -1.14_62E-01,
					        2.00_95E-01,
					        1.08_93E-01,
					        -8.82_47E-02,
					        -3.03_61E-01,
					        -9.86_44E-03,
					    ]							)
				elif torch_device == "cpu":
					snake_case_      		= torch.tensor(
					    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
				else:
					snake_case_      		= 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(__UpperCamelCase ,				__UpperCamelCase ,				rtol=1E-2							)							)







@slow
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__UpperCamelCase							) for s in shape]							)}.npy"""


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0 ,				__UpperCamelCase=(4, 3, 5_12, 5_12) ,				__UpperCamelCase=False							):

				"""simple docstring"""


				snake_case_      		= torch.floataa if fpaa else torch.floataa
				snake_case_      		= torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCamelCase ,				__UpperCamelCase							)							)							).to(__UpperCamelCase							).to(__UpperCamelCase							)
				return image


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase="CompVis/stable-diffusion-v1-4" ,				__UpperCamelCase=False							):

				"""simple docstring"""


				snake_case_      		= 'fp16' if fpaa else None
				snake_case_      		= torch.floataa if fpaa else torch.floataa

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

				return model


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0							):

				"""simple docstring"""


				if torch_device == "mps":
					return torch.manual_seed(__UpperCamelCase							)
				return torch.Generator(device=__UpperCamelCase							).manual_seed(__UpperCamelCase							)


			@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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model()
				snake_case_      		= self.get_sd_image(__UpperCamelCase							)
				snake_case_      		= self.get_generator(__UpperCamelCase							)

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

				assert sample.shape == image.shape

				snake_case_      		= sample[-1, -2:, -2:, :2].flatten().float().cpu()
				snake_case_      		= 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, [-0.0513, 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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model(fpaa=__UpperCamelCase							)
				snake_case_      		= self.get_sd_image(__UpperCamelCase ,				fpaa=__UpperCamelCase							)
				snake_case_      		= self.get_generator(__UpperCamelCase							)

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

				assert sample.shape == image.shape

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

				assert torch_all_close(__UpperCamelCase ,				__UpperCamelCase ,				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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model()
				snake_case_      		= self.get_sd_image(__UpperCamelCase							)

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

				assert sample.shape == image.shape

				snake_case_      		= sample[-1, -2:, -2:, :2].flatten().float().cpu()
				snake_case_      		= 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, [-0.2051, -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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model()
				snake_case_      		= self.get_sd_image(__UpperCamelCase ,				shape=(3, 4, 64, 64)							)

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

				assert list(sample.shape							) == [3, 3, 5_12, 5_12]

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

				assert torch_all_close(__UpperCamelCase ,				__UpperCamelCase ,				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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model(fpaa=__UpperCamelCase							)
				snake_case_      		= self.get_sd_image(__UpperCamelCase ,				shape=(3, 4, 64, 64) ,				fpaa=__UpperCamelCase							)

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

				assert list(sample.shape							) == [3, 3, 5_12, 5_12]

				snake_case_      		= sample[-1, -2:, :2, -2:].flatten().float().cpu()
				snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model(fpaa=__UpperCamelCase							)
				snake_case_      		= self.get_sd_image(__UpperCamelCase ,				shape=(3, 4, 64, 64) ,				fpaa=__UpperCamelCase							)

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

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

				assert list(sample.shape							) == [3, 3, 5_12, 5_12]

				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 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model()
				snake_case_      		= self.get_sd_image(__UpperCamelCase ,				shape=(3, 4, 64, 64)							)

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

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

				assert list(sample.shape							) == [3, 3, 5_12, 5_12]

				assert torch_all_close(__UpperCamelCase ,				__UpperCamelCase ,				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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_sd_vae_model()
				snake_case_      		= self.get_sd_image(__UpperCamelCase							)
				snake_case_      		= self.get_generator(__UpperCamelCase							)

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

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

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

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



from ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)
