




'''simple docstring'''







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard


@slow
@require_flax
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().tearDown()
           gc.collect()







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png'''				)
           UpperCAmelCase			       =		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)

           UpperCAmelCase			       =		'''xvjiarui/stable-diffusion-2-inpainting'''
           UpperCAmelCase       ,     UpperCAmelCase			       =		FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase   ,				safety_checker=lowercase				)

           UpperCAmelCase			       =		'''Face of a yellow cat, high resolution, sitting on a park bench'''

           UpperCAmelCase			       =		jax.random.PRNGKey(0				)
           UpperCAmelCase			       =		50

           UpperCAmelCase			       =		jax.device_count()
           UpperCAmelCase			       =		num_samples * [prompt]
           UpperCAmelCase			       =		num_samples * [init_image]
           UpperCAmelCase			       =		num_samples * [mask_image]
           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		pipeline.prepare_inputs(lowercase   ,				lowercase   ,				lowercase				)

           # shard inputs and rng
           UpperCAmelCase			       =		replicate(lowercase				)
           UpperCAmelCase			       =		jax.random.split(lowercase   ,				jax.device_count()				)
           UpperCAmelCase			       =		shard(lowercase				)
           UpperCAmelCase			       =		shard(lowercase				)
           UpperCAmelCase			       =		shard(lowercase				)

           UpperCAmelCase			       =		pipeline(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				jit=lowercase				)

           UpperCAmelCase			       =		output.images.reshape(lowercase   ,				512   ,				512   ,				3				)

           UpperCAmelCase			       =		images[0, 253:256, 253:256, -1]

           UpperCAmelCase			       =		jnp.asarray(jax.device_get(image_slice.flatten()				)				)
           UpperCAmelCase			       =		jnp.array(
               [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]				)
           print(f"output_slice: {output_slice}"				)
           assert jnp.abs(output_slice - expected_slice				).max() < 1E-2










'''simple docstring'''







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   A     =input('Enter image url: ').strip()
   print(f"""Downloading image from {url} ...""")
   A     =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     =soup.find('meta', {'property': 'og:image'})['content']
   A     =requests.get(image_url).content
   A     =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}.""")




