






'''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_		:	Tuple									=   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.'''    ,		)
				a_		:	List[str]									=   parser.parse_args()
				return args





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]   ):
				if not len(__A   ) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.'''   )

				a_					,      a_		:	int									=   imgs[0].size
				a_		:	List[str]									=   Image.new('''RGB'''    ,		size=(cols * w, rows * h)   )
				a_					,      a_		:	Union[str, Any]									=   grid.size

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





def 		_UpperCAmelCase (				__A				: Dict    ,		__A				: str="robotic cat with wings"    ,		__A				: Any=7.5    ,		__A				: Union[str, Any]=50    ,		__A				: List[Any]=1    ,		__A				: List[str]=42    ,		):
				a_		:	Optional[int]									=   torch.Generator(pipeline.device   ).manual_seed(__A   )
				a_		:	Any									=   pipeline(
				    __A    ,		guidance_scale=__A    ,		num_inference_steps=__A    ,		generator=__A    ,		num_images_per_prompt=__A    ,		).images
				a_		:	int									=   int(math.sqrt(__A   )   )
				a_		:	List[Any]									=   image_grid(__A    ,		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'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




