

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

import requests
from bsa import BeautifulSoup

_a:    Dict 							=			{
    """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      (						A = "dhaka"       ,     A = 5    ):
	UpperCAmelCase_       				=			min(A       ,     50    )  # Prevent abuse!
	UpperCAmelCase_       				=			{
	    "q": query,
	    "tbm": "isch",
	    "hl": "en",
	    "ijn": "0",
	}

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

	UpperCAmelCase_       				=			json.dumps(A    )
	UpperCAmelCase_       				=			json.loads(A    )

	UpperCAmelCase_       				=			re.findall(
	    r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","       ,     A       ,     )
	if not matched_google_image_data:
		return 0

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

	UpperCAmelCase_       				=			re.findall(
	    r"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"       ,     A       ,     )
	for index, fixed_full_res_image in enumerate(A    ):
		if index >= max_images:
			return index
		UpperCAmelCase_       				=			bytes(A       ,     "ascii"    ).decode(
		    "unicode-escape"    )
		UpperCAmelCase_       				=			bytes(A       ,     "ascii"    ).decode(
		    "unicode-escape"    )
		UpperCAmelCase_       				=			urllib.request.build_opener()
		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",
		    )
		]
		urllib.request.install_opener(A    )
		UpperCAmelCase_       				=			F"query_{query.replace(' '       ,     '_'    )}"
		if not os.path.exists(A    ):
			os.makedirs(A    )
		urllib.request.urlretrieve(  # noqa: S310
		    A       ,     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


from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_a:    Optional[int] 							=			"""CompVis/stable-diffusion-v1-1"""
_a:    Dict 							=			"""CompVis/stable-diffusion-v1-2"""
_a:    Optional[Any] 							=			"""CompVis/stable-diffusion-v1-3"""
_a:    str 							=			"""CompVis/stable-diffusion-v1-4"""

class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						AutoencoderKL		,	lowerCAmelCase	:						CLIPTextModel		,	lowerCAmelCase	:						CLIPTokenizer		,	lowerCAmelCase	:						UNetaDConditionModel		,	lowerCAmelCase	:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,	lowerCAmelCase	:						StableDiffusionSafetyChecker		,	lowerCAmelCase	:						CLIPImageProcessor		,	lowerCAmelCase	:						bool = True		,	):


		'''simple docstring'''







		super()._init_()

		UpperCAmelCase_       				=			StableDiffusionPipeline.from_pretrained(lowerCAmelCase							)
		UpperCAmelCase_       				=			StableDiffusionPipeline.from_pretrained(lowerCAmelCase							)
		UpperCAmelCase_       				=			StableDiffusionPipeline.from_pretrained(lowerCAmelCase							)
		UpperCAmelCase_       				=			StableDiffusionPipeline(
		    vae=lowerCAmelCase		,	text_encoder=lowerCAmelCase		,	tokenizer=lowerCAmelCase		,	unet=lowerCAmelCase		,	scheduler=lowerCAmelCase		,	safety_checker=lowerCAmelCase		,	feature_extractor=lowerCAmelCase		,	requires_safety_checker=lowerCAmelCase		,	)

		self.register_modules(pipelinea=self.pipea		,	pipelinea=self.pipea		,	pipelinea=self.pipea		,	pipelinea=self.pipea							)




	@property
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return {k: getattr(self		,	lowerCAmelCase							) for k in self.config.keys() if not k.startswith("_"							)}




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Optional[Union[str, int]] = "auto"							):


		'''simple docstring'''







		if slice_size == "auto":
			# half the attention head size is usually a good trade-off between
			# speed and memory
			UpperCAmelCase_       				=			self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(lowerCAmelCase							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		self.enable_attention_slicing(lowerCAmelCase							)




	@torch.no_grad()
	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, List[str]]		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						float = 7.5		,	lowerCAmelCase	:						Optional[Union[str, List[str]]] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						Optional[torch.FloatTensor] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	**lowerCAmelCase	:						Tuple		,	):


		'''simple docstring'''







		return self.pipea(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)




	@torch.no_grad()
	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Union[str, List[str]]		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						float = 7.5		,	lowerCAmelCase	:						Optional[Union[str, List[str]]] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						Optional[torch.FloatTensor] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		return self.pipea(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)




	@torch.no_grad()
	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Union[str, List[str]]		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						float = 7.5		,	lowerCAmelCase	:						Optional[Union[str, List[str]]] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						Optional[torch.FloatTensor] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	**lowerCAmelCase	:						Union[str, Any]		,	):


		'''simple docstring'''







		return self.pipea(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)




	@torch.no_grad()
	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Union[str, List[str]]		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						float = 7.5		,	lowerCAmelCase	:						Optional[Union[str, List[str]]] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						Optional[torch.FloatTensor] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







		return self.pipea(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)




	@torch.no_grad()
	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, List[str]]		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 512		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						float = 7.5		,	lowerCAmelCase	:						Optional[Union[str, List[str]]] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						Optional[torch.FloatTensor] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	**lowerCAmelCase	:						Union[str, Any]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			"cuda" if torch.cuda.is_available() else "cpu"
		self.to(lowerCAmelCase							)

		# Checks if the height and width are divisible by 8 or not
		if height % 8 != 0 or width % 8 != 0:
			raise ValueError(F"`height` and `width` must be divisible by 8 but are {height} and {width}."							)

		# Get first result from Stable Diffusion Checkpoint v1.1
		UpperCAmelCase_       				=			self.textaimg_sda_a(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)

		# Get first result from Stable Diffusion Checkpoint v1.2
		UpperCAmelCase_       				=			self.textaimg_sda_a(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)

		# Get first result from Stable Diffusion Checkpoint v1.3
		UpperCAmelCase_       				=			self.textaimg_sda_a(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)

		# Get first result from Stable Diffusion Checkpoint v1.4
		UpperCAmelCase_       				=			self.textaimg_sda_a(
		    prompt=lowerCAmelCase		,	height=lowerCAmelCase		,	width=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase		,	guidance_scale=lowerCAmelCase		,	negative_prompt=lowerCAmelCase		,	num_images_per_prompt=lowerCAmelCase		,	eta=lowerCAmelCase		,	generator=lowerCAmelCase		,	latents=lowerCAmelCase		,	output_type=lowerCAmelCase		,	return_dict=lowerCAmelCase		,	callback=lowerCAmelCase		,	callback_steps=lowerCAmelCase		,	**lowerCAmelCase		,	)

		# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
		return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]							)