






'''simple docstring'''


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCamelCase_				: str    	=							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_				: Union[str, Any]    	=							parser.parse_args()


lowerCamelCase_				: str    	=							'''cpu'''
lowerCamelCase_				: Union[str, Any]    	=							'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

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

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

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

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

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

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








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
