
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

UpperCamelCase_   	=	{
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any] = "dhaka"			,							UpperCamelCase:     Dict = 5  ):






	"""simple docstring"""


	__lowerCAmelCase         =							min(UpperCamelCase			,							5_0  )  # Prevent abuse!
	__lowerCAmelCase         =							{
	    "q": query,
	    "tbm": "isch",
	    "hl": "en",
	    "ijn": "0",
	}

	__lowerCAmelCase         =							requests.get("https://www.google.com/search"			,							params=UpperCamelCase			,							headers=UpperCamelCase  )
	__lowerCAmelCase         =							BeautifulSoup(html.text			,							"html.parser"  )
	__lowerCAmelCase         =							"".join(
	    re.findall(R"AF_initDataCallback\(([^<]+)\);"			,							str(soup.select("script"  )  )  )  )

	__lowerCAmelCase         =							json.dumps(UpperCamelCase  )
	__lowerCAmelCase         =							json.loads(UpperCamelCase  )

	__lowerCAmelCase         =							re.findall(
	    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","			,							UpperCamelCase			,							)
	if not matched_google_image_data:
		return 0

	__lowerCAmelCase         =							re.sub(
	    R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"			,							""			,							str(UpperCamelCase  )			,							)

	__lowerCAmelCase         =							re.findall(
	    R"(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]"			,							UpperCamelCase			,							)
	for index, fixed_full_res_image in enumerate(UpperCamelCase  ):
		if index >= max_images:
			return index
		__lowerCAmelCase         =							bytes(UpperCamelCase			,							"ascii"  ).decode(
		    "unicode-escape"  )
		__lowerCAmelCase         =							bytes(UpperCamelCase			,							"ascii"  ).decode(
		    "unicode-escape"  )
		__lowerCAmelCase         =							urllib.request.build_opener()
		__lowerCAmelCase         =							[
		    (
		        "User-Agent",
		        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
		        " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
		    )
		]
		urllib.request.install_opener(UpperCamelCase  )
		__lowerCAmelCase         =							F"query_{query.replace(' '			,							'_'  )}"
		if not os.path.exists(UpperCamelCase  ):
			os.makedirs(UpperCamelCase  )
		urllib.request.urlretrieve(  # noqa: S310
		    UpperCamelCase			,							F"{path_name}/original_size_img_{index}.jpg"  )
	return index


if __name__ == "__main__":
					try:
										UpperCamelCase_   	=	download_images_from_google_query(sys.argv[1])
										print(f'''{image_count} images were downloaded to disk.''')
					except IndexError:
										print("Please provide a search term.")
										raise








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






