





class        __magic_name__	:  # Public class to implement a graph





     """simple docstring"""



     def __init__(				self							:Tuple      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:list[list[bool]]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      row
        A_       :					Tuple        =      col
        A_       :					List[str]        =      graph



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:list[list[bool]]	):





        '''simple docstring'''




        return (
            0 <= i < self.ROW
            and 0 <= j < self.COL
            and not visited[i][j]
            and self.graph[i][j]
        )



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:list[list[bool]]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
        A_       :					List[str]        =      [-1, 0, 1, -1, 1, -1, 0, 1]
        A_       :					List[str]        =      True  # Make those cells visited
        for k in range(8	):
           if self.is_safe(i + row_nbr[k]      ,   j + col_nbr[k]      ,   snake_case	):
              self.diffs(i + row_nbr[k]      ,   j + col_nbr[k]      ,   snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):  # And finally, count all islands.





        '''simple docstring'''




        A_       :					int        =      [[False for j in range(self.COL	)] for i in range(self.ROW	)]
        A_       :					Dict        =      0
        for i in range(self.ROW	):
           for j in range(self.COL	):
              if visited[i][j] is False and self.graph[i][j] == 1:
                 self.diffs(snake_case      ,   snake_case      ,   snake_case	)
                 count += 1
        return count







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   Any						       =						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				:   Tuple						       =						parser.parse_args()


_lowerCAmelCase				:   Any						       =						'''cpu'''
_lowerCAmelCase				:   Dict						       =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_lowerCAmelCase				:   Union[str, Any]						       =						'''path-to-your-trained-model'''
_lowerCAmelCase				:   Tuple						       =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 _lowerCAmelCase				:   List[str]						       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowerCAmelCase				:   List[Any]						       =						pipe.to(device)

# to channels last
_lowerCAmelCase				:   Any						       =						pipe.unet.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   int						       =						pipe.vae.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   Optional[Any]						       =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Optional[int]						       =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowerCAmelCase				:   Tuple						       =						torch.randn(2, 4, 64, 64)
_lowerCAmelCase				:   List[str]						       =						torch.rand(1) * 999
_lowerCAmelCase				:   List[Any]						       =						torch.randn(2, 77, 768)
_lowerCAmelCase				:   int						       =						(sample, timestep, encoder_hidden_status)
try:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 _lowerCAmelCase				:   Optional[Any]						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						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				:   Dict						       =						666
_lowerCAmelCase				:   Tuple						       =						torch.Generator(device).manual_seed(seed)
_lowerCAmelCase				:   Tuple						       =						{'''generator''': generator}
if args.steps is not None:
 _lowerCAmelCase				:   Tuple						       =						args.steps

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

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