





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




			__lowerCamelCase	:       Union[str, Any]							=    AutoencoderKL
			__lowerCamelCase	:       Optional[Any]							=    '''sample'''
			__lowerCamelCase	:       Optional[Any]							=    1E-2






			@property
			def    a_       (			self   :    str   )	->	int:







					"""simple docstring"""






					A__        =   4
					A__        =   3
					A__        =   (32, 32)

					A__        =   floats_tensor((batch_size, num_channels) + sizes   ).to(__lowerCAmelCase   )

					return {"sample": image}






			@property
			def    a_       (			self   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					return (3, 32, 32)






			@property
			def    a_       (			self   :    List[Any]   )	->	int:







					"""simple docstring"""






					return (3, 32, 32)






			def    a_       (			self   :    int   )	->	Any:







					"""simple docstring"""






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






			def    a_       (			self   :    List[str]   )	->	str:







					"""simple docstring"""






					pass






			def    a_       (			self   :    Optional[int]   )	->	Dict:







					"""simple docstring"""






					pass






			@unittest.skipIf(torch_device == """mps"""							,	"""Gradient checkpointing skipped on MPS"""   )
			def    a_       (			self   :    Dict   )	->	int:







					"""simple docstring"""






					A__      ,			A__        =   self.prepare_init_args_and_inputs_for_common()
					A__        =   self.model_class(**__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )

					assert not model.is_gradient_checkpointing and model.training

					A__        =   model(**__lowerCAmelCase   ).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()

					A__        =   torch.randn_like(__lowerCAmelCase   )
					A__        =   (out - labels).mean()
					loss.backward()

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

					assert model_a.is_gradient_checkpointing and model_a.training

					A__        =   model_a(**__lowerCAmelCase   ).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()
					A__        =   (out_a - labels).mean()
					loss_a.backward()

					# compare the output and parameters gradients
					self.assertTrue((loss - loss_a).abs() < 1e-5   )
					A__        =   dict(model.named_parameters()   )
					A__        =   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   :    Dict   )	->	List[str]:







					"""simple docstring"""






					A__      ,			A__        =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""							,	output_loading_info=__lowerCAmelCase   )
					self.assertIsNotNone(__lowerCAmelCase   )
					self.assertEqual(len(loading_info["""missing_keys"""]   )							,	0   )

					model.to(__lowerCAmelCase   )
					A__        =   model(**self.dummy_input   )

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







			def    a_       (			self   :    str   )	->	Tuple:







					"""simple docstring"""






					A__        =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""   )
					A__        =   model.to(__lowerCAmelCase   )
					model.eval()

					if torch_device == "mps":
							A__        =   torch.manual_seed(0   )
					else:
							A__        =   torch.Generator(device=__lowerCAmelCase   ).manual_seed(0   )

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

					A__        =   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":
							A__        =   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":
							A__        =   torch.tensor(
							    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]   )
					else:
							A__        =   torch.tensor(
							    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]   )

					self.assertTrue(torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	rtol=1e-2   )   )


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




			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    Any   )	->	List[Any]:







					"""simple docstring"""






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






			def    a_       (			self   :    List[str]   )	->	Tuple:







					"""simple docstring"""






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






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    Optional[int]=0							,	__lowerCAmelCase   :    Dict=(4, 3, 5_12, 5_12)							,	__lowerCAmelCase   :    List[Any]=False   )	->	Any:







					"""simple docstring"""






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






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Optional[int]="CompVis/stable-diffusion-v1-4"							,	__lowerCAmelCase   :    List[str]=False   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   """fp16""" if fpaa else None
					A__        =   torch.floataa if fpaa else torch.floataa

					A__        =   AutoencoderKL.from_pretrained(
					    __lowerCAmelCase							,	subfolder="""vae"""							,	torch_dtype=__lowerCAmelCase							,	revision=__lowerCAmelCase							,	)
					model.to(__lowerCAmelCase   ).eval()

					return model






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Tuple=0   )	->	Any:







					"""simple docstring"""






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






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
			        [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
			        # fmt: on
			    ]   )
			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model()
					A__        =   self.get_sd_image(__lowerCAmelCase   )
					A__        =   self.get_generator(__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model(__lowerCAmelCase							,	generator=__lowerCAmelCase							,	sample_posterior=__lowerCAmelCase   ).sample

					assert sample.shape == image.shape

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=3e-3   )






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
			        [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
			        # fmt: on
			    ]   )
			@require_torch_gpu
			def    a_       (			self   :    str							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model(fpaa=__lowerCAmelCase   )
					A__        =   self.get_sd_image(__lowerCAmelCase							,	fpaa=__lowerCAmelCase   )
					A__        =   self.get_generator(__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model(__lowerCAmelCase							,	generator=__lowerCAmelCase							,	sample_posterior=__lowerCAmelCase   ).sample

					assert sample.shape == image.shape

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-2   )






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
			        [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
			        # fmt: on
			    ]   )
			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Optional[int]   )	->	int:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model()
					A__        =   self.get_sd_image(__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model(__lowerCAmelCase   ).sample

					assert sample.shape == image.shape

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=3e-3   )






			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
			        [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
			        # fmt: on
			    ]   )
			@require_torch_gpu
			def    a_       (			self   :    int							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Tuple   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model()
					A__        =   self.get_sd_image(__lowerCAmelCase							,	shape=(3, 4, 64, 64)   )

					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

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

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-3   )






			@parameterized.expand(
			    [
			        # fmt: off
			        [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
			        [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
			        # fmt: on
			    ]   )
			@require_torch_gpu
			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int   )	->	List[str]:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model(fpaa=__lowerCAmelCase   )
					A__        =   self.get_sd_image(__lowerCAmelCase							,	shape=(3, 4, 64, 64)							,	fpaa=__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

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

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	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   :    List[str]							,	__lowerCAmelCase   :    int   )	->	Any:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model(fpaa=__lowerCAmelCase   )
					A__        =   self.get_sd_image(__lowerCAmelCase							,	shape=(3, 4, 64, 64)							,	fpaa=__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	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   :    Optional[Any]							,	__lowerCAmelCase   :    str   )	->	int:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model()
					A__        =   self.get_sd_image(__lowerCAmelCase							,	shape=(3, 4, 64, 64)   )

					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
							A__        =   model.decode(__lowerCAmelCase   ).sample

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

					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-2   )







			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
			        [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
			        # fmt: on
			    ]   )
			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Union[str, Any]   )	->	List[Any]:







					"""simple docstring"""






					A__        =   self.get_sd_vae_model()
					A__        =   self.get_sd_image(__lowerCAmelCase   )
					A__        =   self.get_generator(__lowerCAmelCase   )

					with torch.no_grad():
							A__        =   model.encode(__lowerCAmelCase   ).latent_dist
							A__        =   dist.sample(generator=__lowerCAmelCase   )

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

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

					A__        =   3e-3 if torch_device != """mps""" else 1e-2
					assert torch_all_close(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=__lowerCAmelCase   )










from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      BeautifulSoup(requests.get(url).content, '''html.parser''')
					# The image URL is in the content field of the first meta tag with property og:image
					A    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
					with open(file_name, '''wb''') as fp:
										fp.write(image_data)
					print(F'''Done. Image saved to disk as {file_name}.''')



