
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 							__A()		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	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."""						,  )
		_UpperCamelCase						    =	parser.parse_args()
		return args


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






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

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

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


def 							__A(lowerCAmelCase						,  lowerCAmelCase="robotic cat with wings"						,  lowerCAmelCase=7.5						,  lowerCAmelCase=5_0						,  lowerCAmelCase=1						,  lowerCAmelCase=4_2						,  )		->				Optional[int]:

		"""simple docstring"""






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


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

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





lowerCamelCase__	=							[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



