





'''simple docstring'''




import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel




def 						__lowerCamelCase			(	)   ->Dict:
	snake_case__	       =   argparse.ArgumentParser()
	parser.add_argument(
	    '-m'					,				'--pretrained_model_name_or_path'					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				required=UpperCAmelCase_					,				help='Path to pretrained model or model identifier from huggingface.co/models.'					,				)
	parser.add_argument(
	    '-c'					,				'--caption'					,				type=UpperCAmelCase_					,				default='robotic cat with wings'					,				help='Text used to generate images.'					,				)
	parser.add_argument(
	    '-n'					,				'--images_num'					,				type=UpperCAmelCase_					,				default=4					,				help='How much images to generate.'					,				)
	parser.add_argument(
	    '-s'					,				'--seed'					,				type=UpperCAmelCase_					,				default=42					,				help='Seed for random process.'					,				)
	parser.add_argument(
	    '-ci'					,				'--cuda_id'					,				type=UpperCAmelCase_					,				default=0					,				help='cuda_id.'					,				)
	snake_case__	       =   parser.parse_args()
	return args




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[Any]:
	if not len(UpperCAmelCase_							) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'							)

	snake_case__       ,  snake_case__	       =   imgs[0].size
	snake_case__	       =   Image.new('RGB'					,				size=(cols * w, rows * h)							)
	snake_case__       ,  snake_case__	       =   grid.size

	for i, img in enumerate(UpperCAmelCase_							):
		grid.paste(UpperCAmelCase_					,				box=(i % cols * w, i // cols * h)							)
	return grid




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_="robotic cat with wings"					,				UpperCAmelCase_=7.5					,				UpperCAmelCase_=50					,				UpperCAmelCase_=1					,				UpperCAmelCase_=42					,				)   ->int:
	snake_case__	       =   torch.Generator(pipeline.device							).manual_seed(UpperCAmelCase_							)
	snake_case__	       =   pipeline(
	    UpperCAmelCase_					,				guidance_scale=UpperCAmelCase_					,				num_inference_steps=UpperCAmelCase_					,				generator=UpperCAmelCase_					,				num_images_per_prompt=UpperCAmelCase_					,				).images
	snake_case__	       =   int(math.sqrt(UpperCAmelCase_							)							)
	snake_case__	       =   image_grid(UpperCAmelCase_					,				rows=_rows					,				cols=num_images_per_prompt // _rows							)
	return grid, images


a__					:			Any          =     parse_args()
# Load models and create wrapper for stable diffusion
a__					:			Optional[int]          =     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
a__					:			str          =     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
a__					:			Union[str, Any]          =     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
a__					:			List[str]          =     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

a__					:			Union[str, Any]          =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a__					:			int          =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
			a__					:			Optional[Any]          =     load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, '''unet''', unet)
else:
			a__					:			int          =     unet.to(torch.device('''cuda''', args.cuda_id))
a__					:			Optional[Any]          =     pipeline.to(unet.device)
a__      ,       a__					:			Optional[Any]          =     generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
a__					:			Tuple          =     os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))










'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



