




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCamelCase				:		Tuple     =   argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowerCamelCase				:		Optional[int]     =   parser.parse_args()


lowerCamelCase				:		Optional[Any]     =   "cpu"
lowerCamelCase				:		Dict     =   "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowerCamelCase				:		Optional[int]     =   "path-to-your-trained-model"
lowerCamelCase				:		Union[str, Any]     =   StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 lowerCamelCase				:		int     =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCamelCase				:		Optional[Any]     =   pipe.to(device)

# to channels last
lowerCamelCase				:		Tuple     =   pipe.unet.to(memory_format=torch.channels_last)
lowerCamelCase				:		Tuple     =   pipe.vae.to(memory_format=torch.channels_last)
lowerCamelCase				:		List[Any]     =   pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 lowerCamelCase				:		Any     =   pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowerCamelCase				:		int     =   torch.randn(2, 4, 64, 64)
lowerCamelCase				:		Dict     =   torch.rand(1) * 999
lowerCamelCase				:		Any     =   torch.randn(2, 77, 768)
lowerCamelCase				:		Optional[int]     =   (sample, timestep, encoder_hidden_status)
try:
 lowerCamelCase				:		List[str]     =   ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 lowerCamelCase				:		int     =   ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase				:		Union[str, Any]     =   ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase				:		Optional[int]     =   ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
 lowerCamelCase				:		Tuple     =   ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowerCamelCase				:		Union[str, Any]     =   666
lowerCamelCase				:		Any     =   torch.Generator(device).manual_seed(seed)
lowerCamelCase				:		Any     =   {"generator": generator}
if args.steps is not None:
 lowerCamelCase				:		Dict     =   args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
 lowerCamelCase				:		Any     =   pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")









lowerCamelCase				:		Tuple     =   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       int	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [False] * len(lowercase     )
      lowerCamelCase_		     =    [s]
      lowerCamelCase_		     =    True

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

      return visited[t]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Tuple	,							lowercase	:       Tuple     ):


      '''simple docstring'''



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

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

            max_flow += path_flow
            lowerCamelCase_		     =    sink

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



