

"""simple docstring"""


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

import requests
from bsa import BeautifulSoup

__A		  =     {
    "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        a__       (       __SCREAMING_SNAKE_CASE = "dhaka"   ,					__SCREAMING_SNAKE_CASE = 5					)      ->      Optional[Any]:
		__lowerCAmelCase:			Optional[Any]        =     min(_SCREAMING_SNAKE_CASE   ,					5_0					)  # Prevent abuse!
		__lowerCAmelCase:			List[str]        =     {
		    """q""": query,
		    """tbm""": """isch""",
		    """hl""": """en""",
		    """ijn""": """0""",
		}

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

		__lowerCAmelCase:			Union[str, Any]        =     json.dumps(_SCREAMING_SNAKE_CASE					)
		__lowerCAmelCase:			str        =     json.loads(_SCREAMING_SNAKE_CASE					)

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

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

		__lowerCAmelCase:			Optional[int]        =     re.findall(
		    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"   ,					_SCREAMING_SNAKE_CASE   ,					)
		for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE					):
				if index >= max_images:
						return index
				__lowerCAmelCase:			List[Any]        =     bytes(_SCREAMING_SNAKE_CASE   ,					"ascii"					).decode(
				    "unicode-escape"					)
				__lowerCAmelCase:			Union[str, Any]        =     bytes(_SCREAMING_SNAKE_CASE   ,					"ascii"					).decode(
				    "unicode-escape"					)
				__lowerCAmelCase:			int        =     urllib.request.build_opener()
				__lowerCAmelCase:			str        =     [
				    (
				        """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(_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase:			List[Any]        =     F"query_{query.replace(' '   ,					'_'					)}"
				if not os.path.exists(_SCREAMING_SNAKE_CASE					):
						os.makedirs(_SCREAMING_SNAKE_CASE					)
				urllib.request.urlretrieve(  # noqa: S310
				    _SCREAMING_SNAKE_CASE   ,					F"{path_name}/original_size_img_{index}.jpg"					)
		return index


if __name__ == "__main__":
			try:
						__A		  =     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 torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






