



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 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  str							=	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.'       ,     )
   UpperCamelCase    :  int							=	parser.parse_args()
   return args
def 		UpperCamelCase	(   snake_case__       :    Tuple       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    List[str]	)	->     str:
   if not len(snake_case__	) == rows * cols:
      raise ValueError('The specified number of rows and columns are not correct.'	)

   UpperCamelCase	,     UpperCamelCase    :  Dict							=	imgs[0].size
   UpperCamelCase    :  int							=	Image.new('RGB'       ,     size=(cols * w, rows * h)	)
   UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	grid.size

   for i, img in enumerate(snake_case__	):
      grid.paste(snake_case__       ,     box=(i % cols * w, i // cols * h)	)
   return grid
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Optional[Any]="robotic cat with wings"       ,     snake_case__       :    List[str]=7.5       ,     snake_case__       :    List[str]=50       ,     snake_case__       :    Optional[Any]=1       ,     snake_case__       :    Optional[int]=42       ,     )	->     str:
   UpperCamelCase    :  Tuple							=	torch.Generator(pipeline.device	).manual_seed(snake_case__	)
   UpperCamelCase    :  Optional[int]							=	pipeline(
       snake_case__       ,     guidance_scale=snake_case__       ,     num_inference_steps=snake_case__       ,     generator=snake_case__       ,     num_images_per_prompt=snake_case__       ,     ).images
   UpperCamelCase    :  int							=	int(math.sqrt(snake_case__	)	)
   UpperCamelCase    :  Dict							=	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 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    int	)	->     int:
   return int(input_a == input_a == 0	)
def 		UpperCamelCase	(   )	->     None:
   print('Truth Table of NOR Gate:'	)
   print('|   Input 1   |  Input 2  | Output |'	)
   print(F"""|      0      |    0      |  {nor_gate(0       ,     0	)}     |"""	)
   print(F"""|      0      |    1      |  {nor_gate(0       ,     1	)}     |"""	)
   print(F"""|      1      |    0      |  {nor_gate(1       ,     0	)}     |"""	)
   print(F"""|      1      |    1      |  {nor_gate(1       ,     1	)}     |"""	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



