




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



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[Any]:
			if not len(snake_case__       ) == rows * cols:
						raise ValueError("""The specified number of rows and columns are not correct."""       )

			lowerCamelCase   ,     lowerCamelCase       =      imgs[0].size
			lowerCamelCase       =      Image.new("""RGB""" ,				size=(cols * w, rows * h)       )
			lowerCamelCase   ,     lowerCamelCase       =      grid.size

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



def 					a__				(       snake_case__ ,				snake_case__="robotic cat with wings" ,				snake_case__=7.5 ,				snake_case__=50 ,				snake_case__=1 ,				snake_case__=42 ,				)		->      Tuple:
			lowerCamelCase       =      torch.Generator(pipeline.device       ).manual_seed(snake_case__       )
			lowerCamelCase       =      pipeline(
			    snake_case__ ,				guidance_scale=snake_case__ ,				num_inference_steps=snake_case__ ,				generator=snake_case__ ,				num_images_per_prompt=snake_case__ ,				).images
			lowerCamelCase       =      int(math.sqrt(snake_case__       )       )
			lowerCamelCase       =      image_grid(snake_case__ ,				rows=_rows ,				cols=num_images_per_prompt // _rows       )
			return grid, images


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

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









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def 					a__				(       snake_case__       )		->      Dict:
			lowerCamelCase       =      [False] * len(snake_case__       )
			lowerCamelCase       =      [-1] * len(snake_case__       )

			def dfs(snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      True
						lowerCamelCase       =      c
						for u in graph[v]:
									if not visited[u]:
												dfs(snake_case__ ,				1 - c       )

			for i in range(len(snake_case__       )       ):
						if not visited[i]:
									dfs(snake_case__ ,				0       )

			for i in range(len(snake_case__       )       ):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
lowerCAmelCase							:    str  							=							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))



