
"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase__          =	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__          =	parser.parse_args()


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

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

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

# optimize with ipex
UpperCamelCase__          =	torch.randn(2, 4, 64, 64)
UpperCamelCase__          =	torch.rand(1) * 9_99
UpperCamelCase__          =	torch.randn(2, 77, 7_68)
UpperCamelCase__          =	(sample, timestep, encoder_hidden_status)
try:
    UpperCamelCase__          =	ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    UpperCamelCase__          =	ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__          =	ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__          =	ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    UpperCamelCase__          =	ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCamelCase__          =	6_66
UpperCamelCase__          =	torch.Generator(device).manual_seed(seed)
UpperCamelCase__          =	{'generator': generator}
if args.steps is not None:
    UpperCamelCase__          =	args.steps

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

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







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCamelCase__ :   List[str]						=							logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							super().__init__(*snake_case__      ,**snake_case__ )
							self.check_model_type(snake_case__ )






				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {}, {}
							if padding is not None:
										SCREAMING_SNAKE_CASE_   : Any					  =      padding
							if truncation is not None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      truncation
							if top_k is not None:
										SCREAMING_SNAKE_CASE_   : int					  =      top_k
							return preprocess_params, {}, postprocess_params






				def __call__(			self      ,snake_case__      ,snake_case__ = None      ,**snake_case__ ):
							if isinstance(snake_case__      ,(Image.Image, str) ) and isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'image': image, 'question': question}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      super().__call__(snake_case__      ,**snake_case__ )
							return results






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=False      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      load_image(inputs['image'] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.tokenizer(
							    inputs['question']      ,return_tensors=self.framework      ,padding=snake_case__      ,truncation=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processor(images=snake_case__      ,return_tensors=self.framework )
							model_inputs.update(snake_case__ )
							return model_inputs






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model(**snake_case__ )
							return model_outputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=5 ):
							if top_k > self.model.config.num_labels:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model.config.num_labels

							if self.framework == "pt":
										SCREAMING_SNAKE_CASE_   : Any					  =      model_outputs.logits.sigmoid()[0]
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      probs.topk(snake_case__ )
							else:
										raise ValueError(F'Unsupported framework: {self.framework}' )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      scores.tolist()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__      ,snake_case__ )]



