





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__(		):
				_snake_case :			Tuple  						=   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.'	,			)
				_snake_case :			int  						=   parser.parse_args()
				return args

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

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

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

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


lowercase_		:				Optional[Any]				=						parse_args()
# Load models and create wrapper for stable diffusion
lowercase_		:				Dict				=						CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
lowercase_		:				Tuple				=						CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
lowercase_		:				Optional[Any]				=						AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
lowercase_		:				List[str]				=						UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

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













import functools

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):

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

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

				if len(__lowerCAmelCase ) == 0:
								return 0

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

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

				_snake_case :			Optional[int]  						=   set(__lowerCAmelCase )

				@functools.cache
				def dynamic_programming(__lowerCAmelCase ) -> 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()






