


'''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 (						):
   UpperCAmelCase__					:   List[Any]       				=       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=4_2	, help="""Seed for random process."""	, )
   parser.add_argument(
       """-ci"""	, """--cuda_id"""	, type=UpperCamelCase__	, default=0	, help="""cuda_id."""	, )
   UpperCAmelCase__					:   Any       				=       parser.parse_args()
   return args







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

   UpperCAmelCase__					:   str       				=       imgs[0].size
   UpperCAmelCase__					:   Optional[Any]       				=       Image.new("""RGB"""	, size=(cols * w, rows * h)   )
   UpperCAmelCase__					:   int       				=       grid.size

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__="robotic cat with wings"	, UpperCamelCase__=7.5	, UpperCamelCase__=5_0	, UpperCamelCase__=1	, UpperCamelCase__=4_2	, ):
   UpperCAmelCase__					:   str       				=       torch.Generator(pipeline.device   ).manual_seed(UpperCamelCase__   )
   UpperCAmelCase__					:   List[Any]       				=       pipeline(
       UpperCamelCase__	, guidance_scale=UpperCamelCase__	, num_inference_steps=UpperCamelCase__	, generator=UpperCamelCase__	, num_images_per_prompt=UpperCamelCase__	, ).images
   UpperCAmelCase__					:   Optional[Any]       				=       int(math.sqrt(UpperCamelCase__   )   )
   UpperCAmelCase__					:   List[str]       				=       image_grid(UpperCamelCase__	, rows=_rows	, cols=num_images_per_prompt // _rows   )
   return grid, images


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

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

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

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

			if len(UpperCamelCase__   ) == 0:
						return 0

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

			if max(UpperCamelCase__   ) >= 3_6_6:
						raise ValueError("""All days elements should be less than 366"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> int:
						if index > 3_6_5:
									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 + 3_0   )	, )

			return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()