
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 						UpperCAmelCase			(	)		->	int:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	Dict:







				"""simple docstring"""



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

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

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


def 						UpperCAmelCase			(	a_		,   a_="robotic cat with wings"		,   a_=7.5		,   a_=5_0		,   a_=1		,   a_=4_2		,   )		->	Optional[Any]:







				"""simple docstring"""



				__A   			= torch.Generator(pipeline.device						).manual_seed(a_						)
				__A   			= pipeline(
				    a_		,   guidance_scale=a_		,   num_inference_steps=a_		,   generator=a_		,   num_images_per_prompt=a_		,   ).images
				__A   			= int(math.sqrt(a_						)						)
				__A   			= image_grid(a_		,   rows=_rows		,   cols=num_images_per_prompt // _rows						)
				return grid, images


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

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


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
					SCREAMING_SNAKE_CASE							:Any							 =				{
					    'linear': PIL.Image.Resampling.BILINEAR,
					    'bilinear': PIL.Image.Resampling.BILINEAR,
					    'bicubic': PIL.Image.Resampling.BICUBIC,
					    'lanczos': PIL.Image.Resampling.LANCZOS,
					    'nearest': PIL.Image.Resampling.NEAREST,
					}
else:
					SCREAMING_SNAKE_CASE							:int							 =				{
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= (images / 2 + 0.5).clamp(0		,   1						)
				__A   			= images.cpu().permute(0		,   2		,   3		,   1						).float().numpy()
				__A   			= numpy_to_pil(a_						)
				return images


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if images.ndim == 3:
								__A   			= images[None, ...]
				__A   			= (images * 2_5_5).round().astype("uint8"						)
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								__A   			= [Image.fromarray(image.squeeze()		,   mode="L"						) for image in images]
				else:
								__A   			= [Image.fromarray(a_						) for image in images]

				return pil_images
