
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      (	)      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      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=42		,						help="Seed for random process."		,						)
							parser.add_argument(
							    "-ci"		,						"--cuda_id"		,						type=_lowerCamelCase		,						default=0		,						help="cuda_id."		,						)
							__UpperCamelCase			:     List[str]         =      parser.parse_args()
							return args




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Optional[int])      ->					str:






							'''simple docstring'''





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

							__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      imgs[0].size
							__UpperCamelCase			:     Optional[int]         =      Image.new("RGB"		,						size=(cols * w, rows * h))
							__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      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				:    List[str]		,						_lowerCamelCase				:    Optional[Any]="robotic cat with wings"		,						_lowerCamelCase				:    Union[str, Any]=7.5		,						_lowerCamelCase				:    Any=50		,						_lowerCamelCase				:    str=1		,						_lowerCamelCase				:    Tuple=42		,						)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      torch.Generator(pipeline.device).manual_seed(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      pipeline(
							    _lowerCamelCase		,						guidance_scale=_lowerCamelCase		,						num_inference_steps=_lowerCamelCase		,						generator=_lowerCamelCase		,						num_images_per_prompt=_lowerCamelCase		,						).images
							__UpperCamelCase			:     str         =      int(math.sqrt(_lowerCamelCase))
							__UpperCamelCase			:     str         =      image_grid(_lowerCamelCase		,						rows=_rows		,						cols=num_images_per_prompt // _rows)
							return grid, images


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

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

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      Any									= 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')
lowercase						:      Tuple									= parser.parse_args()


lowercase						:      str									= 'cpu'
lowercase						:      List[Any]									= 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase						:      Any									= 'path-to-your-trained-model'
lowercase						:      List[Any]									= StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase						:      Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase						:      Dict									= pipe.to(device)

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

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

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