



'''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			( ):
			'''simple docstring'''





			A_	:						int	=     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."""						,      )
			A_	:						str	=     parser.parse_args()
			return args



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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

			A_,    A_	:						Tuple	=     imgs[0].size
			A_	:						Union[str, Any]	=     Image.new("""RGB"""						,      size=(cols * w, rows * h)    )
			A_,    A_	:						List[str]	=     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						,      ):
			'''simple docstring'''





			A_	:						str	=     torch.Generator(pipeline.device    ).manual_seed(lowerCamelCase__    )
			A_	:						int	=     pipeline(
			    lowerCamelCase__						,      guidance_scale=lowerCamelCase__						,      num_inference_steps=lowerCamelCase__						,      generator=lowerCamelCase__						,      num_images_per_prompt=lowerCamelCase__						,      ).images
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )
			A_	:						Any	=     image_grid(lowerCamelCase__						,      rows=_rows						,      cols=num_images_per_prompt // _rows    )
			return grid, images


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

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


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

  while queue:
    _UpperCAmelCase         =					queue.pop(0						)
    for ind in range(len(graph[u]						)						):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase						)
        _UpperCAmelCase         =					True
        _UpperCAmelCase         =					u

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

    while s != source:
      # Find the minimum value in select path
      _UpperCAmelCase         =					min(lowercase      ,graph[parent[s]][s]						)
      _UpperCAmelCase         =					parent[s]

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
