






"""simple docstring"""
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						(		):
	A_		:    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.""" , )
	A_		:    int						     = parser.parse_args()
	return args






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

	A_     ,  A_		:    Optional[Any]						     = imgs[0].size
	A_		:    Tuple						     = Image.new("""RGB""" , size=(cols * w, rows * h)       )
	A_     ,  A_		:    List[Any]						     = 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__ , snake_case__="robotic cat with wings" , snake_case__=7.5 , snake_case__=50 , snake_case__=1 , snake_case__=42 , ):
	A_		:    Union[str, Any]						     = torch.Generator(pipeline.device       ).manual_seed(snake_case__       )
	A_		:    Union[str, Any]						     = pipeline(
	    snake_case__ , guidance_scale=snake_case__ , num_inference_steps=snake_case__ , generator=snake_case__ , num_images_per_prompt=snake_case__ , ).images
	A_		:    Dict						     = int(math.sqrt(snake_case__       )       )
	A_		:    Union[str, Any]						     = image_grid(snake_case__ , 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  ,     _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)))









"""simple docstring"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

