





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
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	:       Optional[Any]							=    VQModel
			__lowerCamelCase	:       Union[str, Any]							=    '''sample'''






			@property
			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    List[Any]=(32, 32)   )	->	List[Any]:







					"""simple docstring"""






					A__        =   4
					A__        =   3

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

					return {"sample": image}






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







					"""simple docstring"""






					return (3, 32, 32)






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







					"""simple docstring"""






					return (3, 32, 32)






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







					"""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""": 3,
					}
					A__        =   self.dummy_input
					return init_dict, inputs_dict






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







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					A__      ,			A__        =   VQModel.from_pretrained("""fusing/vqgan-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   :    List[Any]   )	->	int:







					"""simple docstring"""






					A__        =   VQModel.from_pretrained("""fusing/vqgan-dummy"""   )
					model.to(__lowerCAmelCase   ).eval()

					torch.manual_seed(0   )
					if torch.cuda.is_available():
							torch.cuda.manual_seed_all(0   )

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

					A__        =   output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					A__        =   torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]   )
					# fmt: on
					self.assertTrue(torch.allclose(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-3   )   )










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}.''')



