





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():
		lowerCAmelCase_												=				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."						,   )
		lowerCAmelCase_												=				parser.parse_args()
		return args

def       A(__a:			Tuple						,   __a:			Tuple						,   __a:			str				):





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

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

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

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


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

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







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      [0 for i in range(r + 1  )]
			# nc0 = 1
			SCREAMING_SNAKE_CASE_   : Dict					  =      1
			for i in range(1  ,							n + 1  ):
						# to compute current row from previous row.
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      min(lowerCamelCase_  ,							lowerCamelCase_  )
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]




print(binomial_coefficient(n=10, r=5))



