


'''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      a (     )    ->      Tuple:



					'''simple docstring'''






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


def      a (     __a					,   __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






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

					UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					imgs[0].size
					UpperCamelCase__  :Dict				   =					Image.new('''RGB'''					,   size=(cols * w, rows * h)  )
					UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					grid.size

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


def      a (     __a					,   __a="robotic cat with wings"					,   __a=7.5					,   __a=50					,   __a=1					,   __a=42					,   )    ->      str:



					'''simple docstring'''






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


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

__snake_case				 =	StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__snake_case				 =	lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
		__snake_case				 =	load(args.pretrained_model_name_or_path, model=unet)
		unet.eval()
		setattr(pipeline, '''unet''', unet)
else:
		__snake_case				 =	unet.to(torch.device('''cuda''', args.cuda_id))
__snake_case				 =	pipeline.to(unet.device)
__snake_case ,   __snake_case				 =	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()))))
__snake_case				 =	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 __future__ import annotations

import math


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      int:



					'''simple docstring'''






					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''  )

					if len(__a  ) == 0:
										raise ValueError('''Scores cannot be empty'''  )

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )

					return min(
					    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )


def      a (     )    ->      None:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__  :List[Any]				   =					math.log(len(__a  )					,   2  )
					print('''Optimal value : '''					,   end=''''''  )
					print(minimax(0					,   0					,   __a					,   __a					,   __a  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()