



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:
   _A      =       argparse.ArgumentParser()
   parser.add_argument(
       '''-m'''	,   '''--pretrained_model_name_or_path'''	,   type=_snake_case	,   default=_snake_case	,   required=_snake_case	,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,   )
   parser.add_argument(
       '''-c'''	,   '''--caption'''	,   type=_snake_case	,   default='''robotic cat with wings'''	,   help='''Text used to generate images.'''	,   )
   parser.add_argument(
       '''-n'''	,   '''--images_num'''	,   type=_snake_case	,   default=4	,   help='''How much images to generate.'''	,   )
   parser.add_argument(
       '''-s'''	,   '''--seed'''	,   type=_snake_case	,   default=42	,   help='''Seed for random process.'''	,   )
   parser.add_argument(
       '''-ci'''	,   '''--cuda_id'''	,   type=_snake_case	,   default=0	,   help='''cuda_id.'''	,   )
   _A      =       parser.parse_args()
   return args

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Dict	,   _snake_case	:Any				)  ->     List[Any]:
   if not len(_snake_case				) == rows * cols:
      raise ValueError('''The specified number of rows and columns are not correct.'''				)

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

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:Union[str, Any]="robotic cat with wings"	,   _snake_case	:List[str]=7.5	,   _snake_case	:Optional[int]=50	,   _snake_case	:List[str]=1	,   _snake_case	:List[str]=42	,   )  ->     List[str]:
   _A      =       torch.Generator(pipeline.device				).manual_seed(_snake_case				)
   _A      =       pipeline(
       _snake_case	,   guidance_scale=_snake_case	,   num_inference_steps=_snake_case	,   generator=_snake_case	,   num_images_per_prompt=_snake_case	,   ).images
   _A      =       int(math.sqrt(_snake_case				)				)
   _A      =       image_grid(_snake_case	,   rows=_rows	,   cols=num_images_per_prompt // _rows				)
   return grid, images


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

UpperCAmelCase_		 =					StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
UpperCAmelCase_		 =					lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
   UpperCAmelCase_		 =					load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, """unet""", unet)
else:
   UpperCAmelCase_		 =					unet.to(torch.device("""cuda""", args.cuda_id))
UpperCAmelCase_		 =					pipeline.to(unet.device)
UpperCAmelCase_ ,UpperCAmelCase_		 =					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_		 =					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_							(	_snake_case	:bytes				)  ->     str:
   return "".join([hex(_snake_case				)[2:].zfill(2				).upper() for byte in list(_snake_case				)]				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     bytes:
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(_snake_case				) % 2) != 0:
      raise ValueError(
          '''Base16 encoded data is invalid:
Data does not have an even number of hex digits.'''				)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(_snake_case				) <= set('''0123456789ABCDEF'''				):
      raise ValueError(
          '''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.'''				)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]	,   16				) for i in range(0	,   len(_snake_case				)	,   2				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



