






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__							(      ):





 '''simple docstring'''







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







def 			A__							(      _a  :					Optional[int]		,  _a  :					List[Any]		,  _a  :					Union[str, Any]	):





 '''simple docstring'''







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

 snake_case__       ,   snake_case__  :    int							       =imgs[0].size
 snake_case__  :    List[str]							       =Image.new("""RGB"""		,  size=(cols * w, rows * h)	)
 snake_case__       ,   snake_case__  :    List[str]							       =grid.size

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







def 			A__							(      _a  :					List[str]		,  _a  :					List[Any]="robotic cat with wings"		,  _a  :					Optional[Any]=7.5		,  _a  :					Union[str, Any]=50		,  _a  :					Tuple=1		,  _a  :					int=42		,  ):





 '''simple docstring'''







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


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

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





 '''simple docstring'''







 if isinstance(_a		,  _a	) and isinstance(_a		,  _a	):
  snake_case__  :    int							       =len(set_a.intersection(_a	)	)

  if alternative_union:
   snake_case__  :    int							       =len(_a	) + len(_a	)
  else:
   snake_case__  :    Tuple							       =len(set_a.union(_a	)	)

  return intersection / union

 if isinstance(_a		,  (list, tuple)	) and isinstance(_a		,  (list, tuple)	):
  snake_case__  :    Optional[int]							       =[element for element in set_a if element in set_b]

  if alternative_union:
   snake_case__  :    Optional[Any]							       =len(_a	) + len(_a	)
   return len(_a	) / union
  else:
   snake_case__  :    Dict							       =set_a + [element for element in set_b if element not in set_a]
   return len(_a	) / len(_a	)

  return len(_a	) / len(_a	)
 return None


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   {"""a""", """b""", """c""", """d""", """e"""}
       __lowerCamelCase	:		Optional[int]										=   {"""c""", """d""", """e""", """f""", """h""", """i"""}
       print(jaccard_similarity(set_a, set_b))





