





from datetime import datetime

import requests





def      _UpperCamelCase  (a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
							UpperCamelCase__					  =      requests.get(base_url + url							).json()[0]["""urls"""][0]["""src"""]
							return requests.get(a__							).content


if __name__ == "__main__":
					UpperCamelCase__           =			input("Enter Video/IGTV url: ").strip()
					UpperCamelCase__           =			f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
					with open(file_name, "wb") as fp:
										fp.write(download_video(url))
					print(f"""Done. Video saved to disk as {file_name}.""")











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 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

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

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

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

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







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      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	)

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

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




