



"""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     _lowercase       (						)		->     Dict:
			__lowerCAmelCase   :						Tuple				= argparse.ArgumentParser()
			parser.add_argument(
			    "-m"      ,"--pretrained_model_name_or_path"      ,type=lowercase_      ,default=lowercase_      ,required=lowercase_      ,help="Path to pretrained model or model identifier from huggingface.co/models."      ,)
			parser.add_argument(
			    "-c"      ,"--caption"      ,type=lowercase_      ,default="robotic cat with wings"      ,help="Text used to generate images."      ,)
			parser.add_argument(
			    "-n"      ,"--images_num"      ,type=lowercase_      ,default=4      ,help="How much images to generate."      ,)
			parser.add_argument(
			    "-s"      ,"--seed"      ,type=lowercase_      ,default=42      ,help="Seed for random process."      ,)
			parser.add_argument(
			    "-ci"      ,"--cuda_id"      ,type=lowercase_      ,default=0      ,help="cuda_id."      ,)
			__lowerCAmelCase   :						Union[str, Any]				= parser.parse_args()
			return args


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:

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

			__lowerCAmelCase   :						int				= imgs[0].size
			__lowerCAmelCase   :						Optional[int]				= Image.new("RGB"      ,size=(cols * w, rows * h)			)
			__lowerCAmelCase   :						List[str]				= grid.size

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


def     _lowercase       (						__snake_case      ,__snake_case="robotic cat with wings"      ,__snake_case=7.5      ,__snake_case=50      ,__snake_case=1      ,__snake_case=42      ,)		->     Any:
			__lowerCAmelCase   :						List[Any]				= torch.Generator(pipeline.device			).manual_seed(lowercase_			)
			__lowerCAmelCase   :						Optional[Any]				= pipeline(
			    lowercase_      ,guidance_scale=lowercase_      ,num_inference_steps=lowercase_      ,generator=lowercase_      ,num_images_per_prompt=lowercase_      ,).images
			__lowerCAmelCase   :						List[Any]				= int(math.sqrt(lowercase_			)			)
			__lowerCAmelCase   :						Any				= image_grid(lowercase_      ,rows=_rows      ,cols=num_images_per_prompt // _rows			)
			return grid, images


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

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




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()