




"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     ["""image_processor""", """tokenizer"""]
			lowerCamelCase__ =     """CLIPImageProcessor"""
			lowerCamelCase__ =     ("""CLIPTokenizer""", """CLIPTokenizerFast""")
			def __init__(    self    , lowercase=None    , lowercase=None    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'    , lowercase    , )
											_lowerCamelCase	:     List[str]       =       kwargs.pop('feature_extractor'    )

							_lowerCamelCase	:     Union[str, Any]       =       image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('You need to specify an `image_processor`.'    )
							if tokenizer is None:
											raise ValueError('You need to specify a `tokenizer`.'    )

							super().__init__(lowercase    , lowercase    )
			def __call__(    self    , lowercase=None    , lowercase=None    , lowercase=None    , **lowercase    ):

							if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'    )

							if text is not None:
											_lowerCamelCase	:     Optional[int]       =       self.tokenizer(lowercase    , return_tensors=lowercase    , **lowercase    )

							if images is not None:
											_lowerCamelCase	:     Union[str, Any]       =       self.image_processor(lowercase    , return_tensors=lowercase    , **lowercase    )

							if text is not None and images is not None:
											_lowerCamelCase	:     List[str]       =       image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowercase    )    , tensor_type=lowercase    )
			def    A_       (    self    , *lowercase    , **lowercase    ):
							return self.tokenizer.batch_decode(*lowercase    , **lowercase    )
			def    A_       (    self    , *lowercase    , **lowercase    ):
							return self.tokenizer.decode(*lowercase    , **lowercase    )
			@property
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.tokenizer.model_input_names
							_lowerCamelCase	:     str       =       self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
			@property
			def    A_       (    self    ):
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    , lowercase    , )
							return self.image_processor_class






			@property
			def    A_       (    self    ):
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    , lowercase    , )
							return self.image_processor





"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		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""")
lowercase__			=		parser.parse_args()


lowercase__			=		"""cpu"""
lowercase__			=		"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

lowercase__			=		"""path-to-your-trained-model"""
lowercase__			=		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
			lowercase__			=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__			=		pipe.to(device)

# to channels last
lowercase__			=		pipe.unet.to(memory_format=torch.channels_last)
lowercase__			=		pipe.vae.to(memory_format=torch.channels_last)
lowercase__			=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
			lowercase__			=		pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase__			=		666
lowercase__			=		torch.Generator(device).manual_seed(seed)
lowercase__			=		{"""generator""": generator}
if args.steps is not None:
			lowercase__			=		args.steps

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

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