





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			(      ):
       _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=42    ,       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase="robotic cat with wings"    ,       lowerCAmelCase=7.5    ,       lowerCAmelCase=50    ,       lowerCAmelCase=1    ,       lowerCAmelCase=42    ,       ):
       _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


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

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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






