


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						(							)       ->							Optional[int]:
   __lowerCamelCase:				Union[str, Any]     =	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."""						,      )
   __lowerCamelCase:				Tuple     =	parser.parse_args()
   return args



def     __lowerCAmelCase						(							snake_case	:    List[Any]						,      snake_case	:    Optional[Any]						,      snake_case	:    Union[str, Any] )       ->							Any:
   if not len(snake_case ) == rows * cols:
      raise ValueError("""The specified number of rows and columns are not correct.""" )

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

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



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


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

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






_A		:				Tuple									=       '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				Optional[int]     =	input("""Enter message: """ )
   __lowerCamelCase:				Dict     =	input("""Enter key [alphanumeric]: """ )
   __lowerCamelCase:				List[Any]     =	input("""Encrypt/Decrypt [e/d]: """ )

   if mode.lower().startswith("""e""" ):
      __lowerCamelCase:				Optional[int]     =	"""encrypt"""
      __lowerCamelCase:				Optional[int]     =	encrypt_message(snake_case						,      snake_case )
   elif mode.lower().startswith("""d""" ):
      __lowerCamelCase:				Union[str, Any]     =	"""decrypt"""
      __lowerCamelCase:				Optional[Any]     =	decrypt_message(snake_case						,      snake_case )

   print(f'\n{mode.title()}ed message:' )
   print(snake_case )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """encrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """decrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str )       ->							str:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Optional[int]     =	0
   __lowerCamelCase:				Any     =	key.upper()

   for symbol in message:
      __lowerCamelCase:				int     =	LETTERS.find(symbol.upper() )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index] )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index] )

         num %= len(snake_case )

         if symbol.isupper():
            translated.append(LETTERS[num] )
         elif symbol.islower():
            translated.append(LETTERS[num].lower() )

         key_index += 1
         if key_index == len(snake_case ):
            __lowerCamelCase:				Union[str, Any]     =	0
      else:
         translated.append(snake_case )
   return "".join(snake_case )


if __name__ == "__main__":
   main()


