"""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    (   )							->      str:

	'''simple docstring'''





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

	'''simple docstring'''





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

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

	for i, img in enumerate(UpperCAmelCase_  ):
		grid.paste(UpperCAmelCase_      ,							box=(i % cols * w, i // cols * h)  )
	return grid
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    int="robotic cat with wings"      ,							UpperCAmelCase_   :    Optional[int]=7.5      ,							UpperCAmelCase_   :    str=50      ,							UpperCAmelCase_   :    List[str]=1      ,							UpperCAmelCase_   :    List[Any]=42      ,							)							->      List[Any]:

	'''simple docstring'''





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


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

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

"""simple docstring"""







from decimal import Decimal, getcontext
from math import ceil, factorial
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ):
		raise TypeError('Undefined for non-integers'  )
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'  )

	__snake_case      :							Any						=			precision
	__snake_case      :							Optional[int]						=			ceil(precision / 14  )
	__snake_case      :							List[Any]						=			42_68_80 * Decimal(1_00_05  ).sqrt()
	__snake_case      :							Optional[Any]						=			1
	__snake_case      :							Union[str, Any]						=			13_59_14_09
	__snake_case      :							int						=			Decimal(UpperCAmelCase_  )
	for k in range(1      ,							UpperCAmelCase_  ):
		__snake_case      :							Union[str, Any]						=			factorial(6 * k  ) // (factorial(3 * k  ) * factorial(UpperCAmelCase_  ) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
	return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
	_a					:					List[Any]=			50
	print(f'''The first {n} digits of pi is: {pi(n)}''')





