






'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCAmelCase__						:Union[str, 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""")
UpperCAmelCase__						:Dict          =			parser.parse_args()


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

UpperCAmelCase__						:List[Any]          =			"""path-to-your-trained-model"""
UpperCAmelCase__						:Dict          =			StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   UpperCAmelCase__						:int          =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase__						:Dict          =			pipe.to(device)

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

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

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

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

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











'''simple docstring'''

UpperCAmelCase__						:List[Any]          =			256
# Modulus to hash a string
UpperCAmelCase__						:str          =			1_000_003
def     __lowercase  (_lowercase,						_lowercase     )   ->     bool:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				len(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				len(_lowercase     )
     if p_len > t_len:
          return False

     __lowerCamelCase						:       Optional[Any]				   =				0
     __lowerCamelCase						:       Tuple				   =				0
     __lowerCamelCase						:       Dict				   =				1

     # Calculating the hash of pattern and substring of text
     for i in range(_lowercase     ):
          __lowerCamelCase						:       Optional[int]				   =				(ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
          __lowerCamelCase						:       Dict				   =				(ord(text[i]     ) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          __lowerCamelCase						:       Dict				   =				(modulus_power * alphabet_size) % modulus

     for i in range(0,						t_len - p_len + 1     ):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          __lowerCamelCase						:       Dict				   =				(
              (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
              + ord(text[i + p_len]     )
          ) % modulus
     return False
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				"""abc1abc12"""
     __lowerCamelCase						:       Optional[Any]				   =				"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
     __lowerCamelCase						:       List[Any]				   =				"""alskfjaldsk23adsfabcabc"""
     assert rabin_karp(_lowercase,						_lowercase     ) and not rabin_karp(_lowercase,						_lowercase     )

     # Test 2)
     __lowerCamelCase						:       Optional[int]				   =				"""ABABX"""
     __lowerCamelCase						:       Dict				   =				"""ABABZABABYABABX"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 3)
     __lowerCamelCase						:       Any				   =				"""AAAB"""
     __lowerCamelCase						:       int				   =				"""ABAAAAAB"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 4)
     __lowerCamelCase						:       Any				   =				"""abcdabcy"""
     __lowerCamelCase						:       Dict				   =				"""abcxabcdabxabcdabcdabcy"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 5)
     __lowerCamelCase						:       str				   =				"""Lü"""
     __lowerCamelCase						:       str				   =				"""Lüsai"""
     assert rabin_karp(_lowercase,						_lowercase     )
     __lowerCamelCase						:       Tuple				   =				"""Lue"""
     assert not rabin_karp(_lowercase,						_lowercase     )
     print("""Success."""     )


if __name__ == "__main__":
   test_rabin_karp()



