





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__()       ->   Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__              =	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.''' ,)
	lowerCamelCase__              =	parser.parse_args()
	return args





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







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

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

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





def lowerCAmelCase__(__snake_case ,__snake_case="robotic cat with wings" ,__snake_case=7.5 ,__snake_case=50 ,__snake_case=1 ,__snake_case=42 ,)       ->   Optional[Any]:







	'''simple docstring'''







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


_a						=   parse_args()
# Load models and create wrapper for stable diffusion
_a						=   CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
_a						=   CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
_a						=   AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
_a						=   UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

_a						=   StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_a						=   lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
			_a						=   load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, "unet", unet)
else:
			_a						=   unet.to(torch.device("cuda", args.cuda_id))
_a						=   pipeline.to(unet.device)
_a		,	_a						=   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						=   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)))







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



