





'''simple docstring'''






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  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   Tuple				     =   AutoencoderKL
		lowercase__:   int				     =   '''sample'''
		lowercase__:   Dict				     =   1e-2

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     4
					__snake_case					:      Optional[Any]							      =     3
					__snake_case					:      Optional[Any]							      =     (32, 32)

					__snake_case					:      List[Any]							      =     floats_tensor((batch_size, num_channels) + sizes			).to(__magic_name__			)

					return {"sample": image}

		@property
		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					return (3, 32, 32)

		@property
		def 	lowercase__     (   self					:   str			)					->    Dict:


					"""simple docstring"""

					return (3, 32, 32)

		def 	lowercase__     (   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					__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,
					}
					__snake_case					:      List[Any]							      =     self.dummy_input
					return init_dict, inputs_dict

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		@unittest.skipIf(torch_device == """mps"""  ,      """Gradient checkpointing skipped on MPS"""			)
		def 	lowercase__     (   self					:   Any			)					->    List[Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.prepare_init_args_and_inputs_for_common()
					__snake_case					:      int							      =     self.model_class(**__magic_name__			)
					model.to(__magic_name__			)

					assert not model.is_gradient_checkpointing and model.training

					__snake_case					:      Any							      =     model(**__magic_name__			).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					:      List[str]							      =     torch.randn_like(__magic_name__			)
					__snake_case					:      Dict							      =     (out - labels).mean()
					loss.backward()

					# re-instantiate the model now enabling gradient checkpointing
					__snake_case					:      Optional[int]							      =     self.model_class(**__magic_name__			)
					# clone model
					model_a.load_state_dict(model.state_dict()			)
					model_a.to(__magic_name__			)
					model_a.enable_gradient_checkpointing()

					assert model_a.is_gradient_checkpointing and model_a.training

					__snake_case					:      Dict							      =     model_a(**__magic_name__			).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					:      Optional[Any]							      =     (out_a - labels).mean()
					loss_a.backward()

					# compare the output and parameters gradients
					self.assertTrue((loss - loss_a).abs() < 1E-5			)
					__snake_case					:      Any							      =     dict(model.named_parameters()			)
					__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 	lowercase__     (   self					:   int			)					->    str:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""  ,      output_loading_info=__magic_name__			)
					self.assertIsNotNone(__magic_name__			)
					self.assertEqual(len(loading_info["""missing_keys"""]			)  ,      0			)

					model.to(__magic_name__			)
					__snake_case					:      Tuple							      =     model(**self.dummy_input			)

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

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


					"""simple docstring"""

					__snake_case					:      Any							      =     AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""			)
					__snake_case					:      Optional[int]							      =     model.to(__magic_name__			)
					model.eval()

					if torch_device == "mps":
								__snake_case					:      Optional[Any]							      =     torch.manual_seed(0			)
					else:
								__snake_case					:      Optional[int]							      =     torch.Generator(device=__magic_name__			).manual_seed(0			)

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

					__snake_case					:      int							      =     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					:      Optional[int]							      =     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":
								__snake_case					:      List[Any]							      =     torch.tensor(
								    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]			)
					else:
								__snake_case					:      str							      =     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(__magic_name__  ,      __magic_name__  ,      rtol=1E-2			)			)





@slow
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   str			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   str=0  ,      __magic_name__					:   Tuple=(4, 3, 5_12, 5_12)  ,      __magic_name__					:   Optional[int]=False			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     torch.floataa if fpaa else torch.floataa
					__snake_case					:      str							      =     torch.from_numpy(load_hf_numpy(self.get_file_format(__magic_name__  ,      __magic_name__			)			)			).to(__magic_name__			).to(__magic_name__			)
					return image

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   Any="CompVis/stable-diffusion-v1-4"  ,      __magic_name__					:   List[Any]=False			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     """fp16""" if fpaa else None
					__snake_case					:      Tuple							      =     torch.floataa if fpaa else torch.floataa

					__snake_case					:      Dict							      =     AutoencoderKL.from_pretrained(
					    __magic_name__  ,      subfolder="""vae"""  ,      torch_dtype=__magic_name__  ,      revision=__magic_name__  ,      )
					model.to(__magic_name__			).eval()

					return model

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   Tuple=0			)					->    int:


					"""simple docstring"""

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

		@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 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.get_sd_vae_model()
					__snake_case					:      int							      =     self.get_sd_image(__magic_name__			)
					__snake_case					:      Tuple							      =     self.get_generator(__magic_name__			)

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

					assert sample.shape == image.shape

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

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   str  ,      __magic_name__					:   Any  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.get_sd_vae_model(fpaa=__magic_name__			)
					__snake_case					:      int							      =     self.get_sd_image(__magic_name__  ,      fpaa=__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     self.get_generator(__magic_name__			)

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

					assert sample.shape == image.shape

					__snake_case					:      List[str]							      =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
					__snake_case					:      int							      =     torch.tensor(__magic_name__			)

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     self.get_sd_vae_model()
					__snake_case					:      List[Any]							      =     self.get_sd_image(__magic_name__			)

					with torch.no_grad():
								__snake_case					:      str							      =     model(__magic_name__			).sample

					assert sample.shape == image.shape

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

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.get_sd_vae_model()
					__snake_case					:      Tuple							      =     self.get_sd_image(__magic_name__  ,      shape=(3, 4, 64, 64)			)

					with torch.no_grad():
								__snake_case					:      Dict							      =     model.decode(__magic_name__			).sample

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

					__snake_case					:      int							      =     sample[-1, -2:, :2, -2:].flatten().cpu()
					__snake_case					:      Optional[Any]							      =     torch.tensor(__magic_name__			)

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Dict			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.get_sd_vae_model(fpaa=__magic_name__			)
					__snake_case					:      Optional[Any]							      =     self.get_sd_image(__magic_name__  ,      shape=(3, 4, 64, 64)  ,      fpaa=__magic_name__			)

					with torch.no_grad():
								__snake_case					:      List[str]							      =     model.decode(__magic_name__			).sample

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

					__snake_case					:      str							      =     sample[-1, -2:, :2, -2:].flatten().float().cpu()
					__snake_case					:      Optional[Any]							      =     torch.tensor(__magic_name__			)

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   Any  ,      __magic_name__					:   Tuple			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.get_sd_vae_model(fpaa=__magic_name__			)
					__snake_case					:      Optional[int]							      =     self.get_sd_image(__magic_name__  ,      shape=(3, 4, 64, 64)  ,      fpaa=__magic_name__			)

					with torch.no_grad():
								__snake_case					:      Any							      =     model.decode(__magic_name__			).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
								__snake_case					:      int							      =     model.decode(__magic_name__			).sample

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

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.get_sd_vae_model()
					__snake_case					:      Dict							      =     self.get_sd_image(__magic_name__  ,      shape=(3, 4, 64, 64)			)

					with torch.no_grad():
								__snake_case					:      List[str]							      =     model.decode(__magic_name__			).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
								__snake_case					:      List[Any]							      =     model.decode(__magic_name__			).sample

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

					assert torch_all_close(__magic_name__  ,      __magic_name__  ,      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 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   str			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.get_sd_vae_model()
					__snake_case					:      Optional[int]							      =     self.get_sd_image(__magic_name__			)
					__snake_case					:      List[Any]							      =     self.get_generator(__magic_name__			)

					with torch.no_grad():
								__snake_case					:      List[Any]							      =     model.encode(__magic_name__			).latent_dist
								__snake_case					:      Optional[Any]							      =     dist.sample(generator=__magic_name__			)

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

					__snake_case					:      int							      =     sample[0, -1, -3:, -3:].flatten().cpu()
					__snake_case					:      Tuple							      =     torch.tensor(__magic_name__			)

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









'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


