

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Optional[Any]										=				IFInpaintingPipeline
							_UpperCAmelCase   :  Tuple										=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							_UpperCAmelCase   :  Any										=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_UpperCAmelCase   :  Any										=				PipelineTesterMixin.required_optional_params - {'''latents'''}
							def    lowerCAmelCase					(		self		:      List[Any]):
								return self._get_dummy_components()
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str=0):
								if str(SCREAMING_SNAKE_CASE__).startswith('mps'):
									__lowerCamelCase    :    Any 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase    :    Optional[int] 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			floats_tensor((1, 3, 3_2, 3_2)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			floats_tensor((1, 3, 3_2, 3_2)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs
							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()       ,reason='XFormers attention is only available with CUDA and `xformers` installed'       ,)
							def    lowerCAmelCase					(		self		:      Tuple):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
							def    lowerCAmelCase					(		self		:      List[str]):
								self._test_save_load_optional_components()
							@unittest.skipIf(torch_device != 'cuda'       ,reason='float16 requires CUDA')
							def    lowerCAmelCase					(		self		:      Tuple):
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
							def    lowerCAmelCase					(		self		:      str):
								self._test_save_load_local()


							def    lowerCAmelCase					(		self		:      List[str]):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2       ,)







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=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""")
a		=parser.parse_args()


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

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

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

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

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

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

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




