


"""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      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					argparse.ArgumentParser()
							parser.add_argument(
							    """-m"""       ,			"""--pretrained_model_name_or_path"""       ,			type=SCREAMING_SNAKE_CASE_       ,			default=SCREAMING_SNAKE_CASE_       ,			required=SCREAMING_SNAKE_CASE_       ,			help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,			)
							parser.add_argument(
							    """-c"""       ,			"""--caption"""       ,			type=SCREAMING_SNAKE_CASE_       ,			default="""robotic cat with wings"""       ,			help="""Text used to generate images."""       ,			)
							parser.add_argument(
							    """-n"""       ,			"""--images_num"""       ,			type=SCREAMING_SNAKE_CASE_       ,			default=4       ,			help="""How much images to generate."""       ,			)
							parser.add_argument(
							    """-s"""       ,			"""--seed"""       ,			type=SCREAMING_SNAKE_CASE_       ,			default=4_2       ,			help="""Seed for random process."""       ,			)
							parser.add_argument(
							    """-ci"""       ,			"""--cuda_id"""       ,			type=SCREAMING_SNAKE_CASE_       ,			default=0       ,			help="""cuda_id."""       ,			)
							__UpperCAmelCase   :						List[str]      							=					parser.parse_args()
							return args
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Union[str, Any]							)		->       str:

							'''simple docstring'''



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

							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					imgs[0].size
							__UpperCAmelCase   :						Tuple      							=					Image.new("""RGB"""       ,			size=(cols * w, rows * h)							)
							__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					grid.size

							for i, img in enumerate(SCREAMING_SNAKE_CASE_							):
														grid.paste(SCREAMING_SNAKE_CASE_       ,			box=(i % cols * w, i // cols * h)							)
							return grid
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Any="robotic cat with wings"       ,			_UpperCamelCase		:      List[str]=7.5       ,			_UpperCamelCase		:      int=5_0       ,			_UpperCamelCase		:      Optional[Any]=1       ,			_UpperCamelCase		:      str=4_2       ,			)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					torch.Generator(pipeline.device							).manual_seed(SCREAMING_SNAKE_CASE_							)
							__UpperCAmelCase   :						str      							=					pipeline(
							    SCREAMING_SNAKE_CASE_       ,			guidance_scale=SCREAMING_SNAKE_CASE_       ,			num_inference_steps=SCREAMING_SNAKE_CASE_       ,			generator=SCREAMING_SNAKE_CASE_       ,			num_images_per_prompt=SCREAMING_SNAKE_CASE_       ,			).images
							__UpperCAmelCase   :						Optional[int]      							=					int(math.sqrt(SCREAMING_SNAKE_CASE_							)							)
							__UpperCAmelCase   :						Optional[int]      							=					image_grid(SCREAMING_SNAKE_CASE_       ,			rows=_rows       ,			cols=num_images_per_prompt // _rows							)
							return grid, images


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

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




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


