

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        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	__lowerCamelCase    :    int 	=			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.'     ,			)
	__lowerCamelCase    :    Optional[Any] 	=			parser.parse_args()
	return args






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	if not len(lowerCamelCase__      ) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'      )

	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			imgs[0].size
	__lowerCamelCase    :    int 	=			Image.new('RGB'     ,			size=(cols * w, rows * h)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			grid.size

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__="robotic cat with wings"     ,			lowerCamelCase__=7.5     ,			lowerCamelCase__=5_0     ,			lowerCamelCase__=1     ,			lowerCamelCase__=4_2     ,			)  ->					str:
	__lowerCamelCase    :    int 	=			torch.Generator(pipeline.device      ).manual_seed(lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			pipeline(
	    lowerCamelCase__     ,			guidance_scale=lowerCamelCase__     ,			num_inference_steps=lowerCamelCase__     ,			generator=lowerCamelCase__     ,			num_images_per_prompt=lowerCamelCase__     ,			).images
	__lowerCamelCase    :    Optional[int] 	=			int(math.sqrt(lowerCamelCase__      )      )
	__lowerCamelCase    :    Union[str, Any] 	=			image_grid(lowerCamelCase__     ,			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)))







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




