"""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 						a_		(      ):
				UpperCAmelCase__    =							argparse.ArgumentParser()
				parser.add_argument(
				    '-m'      ,    '--pretrained_model_name_or_path'      ,    type=lowerCamelCase      ,    default=lowerCamelCase      ,    required=lowerCamelCase      ,    help='Path to pretrained model or model identifier from huggingface.co/models.'      ,    )
				parser.add_argument(
				    '-c'      ,    '--caption'      ,    type=lowerCamelCase      ,    default='robotic cat with wings'      ,    help='Text used to generate images.'      ,    )
				parser.add_argument(
				    '-n'      ,    '--images_num'      ,    type=lowerCamelCase      ,    default=4      ,    help='How much images to generate.'      ,    )
				parser.add_argument(
				    '-s'      ,    '--seed'      ,    type=lowerCamelCase      ,    default=4_2      ,    help='Seed for random process.'      ,    )
				parser.add_argument(
				    '-ci'      ,    '--cuda_id'      ,    type=lowerCamelCase      ,    default=0      ,    help='cuda_id.'      ,    )
				UpperCAmelCase__    =							parser.parse_args()
				return args



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if not len(lowerCamelCase ) == rows * cols:
								raise ValueError('The specified number of rows and columns are not correct.' )

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

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



def 						a_		(      lowerCamelCase      ,    lowerCamelCase="robotic cat with wings"      ,    lowerCamelCase=7.5      ,    lowerCamelCase=5_0      ,    lowerCamelCase=1      ,    lowerCamelCase=4_2      ,    ):
				UpperCAmelCase__    =							torch.Generator(pipeline.device ).manual_seed(lowerCamelCase )
				UpperCAmelCase__    =							pipeline(
				    lowerCamelCase      ,    guidance_scale=lowerCamelCase      ,    num_inference_steps=lowerCamelCase      ,    generator=lowerCamelCase      ,    num_images_per_prompt=lowerCamelCase      ,    ).images
				UpperCAmelCase__    =							int(math.sqrt(lowerCamelCase ) )
				UpperCAmelCase__    =							image_grid(lowerCamelCase      ,    rows=_rows      ,    cols=num_images_per_prompt // _rows )
				return grid, images


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

lowerCAmelCase__						:						Optional[Any]		  =			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__						:						int		  =			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
						lowerCAmelCase__						:						int		  =			load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, 'unet', unet)
else:
						lowerCAmelCase__						:						List[Any]		  =			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__						:						List[Any]		  =			pipeline.to(unet.device)
lowerCAmelCase__  ,			lowerCAmelCase__						:						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()))))
lowerCAmelCase__						:						Union[str, Any]		  =			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 socket



def 						a_		(      ):
				UpperCAmelCase__    =							socket.socket(socket.AF_INET      ,    socket.SOCK_STREAM )
				UpperCAmelCase__    =							socket.gethostname()
				UpperCAmelCase__    =							1_2_3_1_2

				sock.connect((host, port) )
				sock.send(b'Hello server!' )

				with open('Received_file'      ,    'wb' ) as out_file:
								print('File opened' )
								print('Receiving data...' )
								while True:
												UpperCAmelCase__    =							sock.recv(1_0_2_4 )
												if not data:
																break
												out_file.write(lowerCamelCase )

				print('Successfully received the file' )
				sock.close()
				print('Connection closed' )


if __name__ == "__main__":
						main()



