


"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					torch.FloatTensor
							__UpperCAmelCase		:					torch.FloatTensor
							__UpperCAmelCase		:					Optional[torch.FloatTensor]          =None
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          =2

							@register_to_config
							def __init__(  self					,     __a = 0.0_2					,     __a = 1_00					,     __a = 1.0_0_7					,     __a = 80					,     __a = 0.0_5					,     __a = 50					,     ):
										# standard deviation of the initial noise distribution
										__lowerCAmelCase       					=    sigma_max

										# setable values
										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    None  # sigma(t_i)

							def     snake_case       (  self					,     __a					,     __a = None			):
										return sample

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    num_inference_steps
										__lowerCAmelCase       					=    np.arange(0					,     self.num_inference_steps			)[::-1].copy()
										__lowerCAmelCase       					=    torch.from_numpy(__a			).to(__a			)
										__lowerCAmelCase       					=    [
										    (
										        self.config.sigma_max**2
										        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
										    )
										    for i in self.timesteps
										]
										__lowerCAmelCase       					=    torch.tensor(__a					,     dtype=torch.floataa					,     device=__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = None			):
										if self.config.s_min <= sigma <= self.config.s_max:
													__lowerCAmelCase       					=    min(self.config.s_churn / self.num_inference_steps					,     2**0.5 - 1			)
										else:
													__lowerCAmelCase       					=    0

										# sample eps ~ N(0, S_noise^2 * I)
										__lowerCAmelCase       					=    self.config.s_noise * randn_tensor(sample.shape					,     generator=__a			).to(sample.device			)
										__lowerCAmelCase       					=    sigma + gamma * sigma
										__lowerCAmelCase       					=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

										return sample_hat, sigma_hat

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a = True					,     ):
										__lowerCAmelCase       					=    sample_hat + sigma_hat * model_output
										__lowerCAmelCase       					=    (sample_hat - pred_original_sample) / sigma_hat
										__lowerCAmelCase       					=    sample_hat + (sigma_prev - sigma_hat) * derivative

										if not return_dict:
													return (sample_prev, derivative)

										return KarrasVeOutput(
										    prev_sample=__a					,     derivative=__a					,     pred_original_sample=__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a = True					,     ):
										__lowerCAmelCase       					=    sample_prev + sigma_prev * model_output
										__lowerCAmelCase       					=    (sample_prev - pred_original_sample) / sigma_prev
										__lowerCAmelCase       					=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

										if not return_dict:
													return (sample_prev, derivative)

										return KarrasVeOutput(
										    prev_sample=__a					,     derivative=__a					,     pred_original_sample=__a			)



							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										raise NotImplementedError()








"""simple docstring"""


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

import requests
from bsa import BeautifulSoup

A  :     Union[str, Any]    					=    {
    "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 _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # 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:
		A  :     Any    					=    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




