


"""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      _snake_case						(						):
      lowerCAmelCase					:	Dict       =   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					:	List[str]       =   parser.parse_args()
      return args


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

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

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


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


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

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





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


