



# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class 	lowerCamelCase__ (		_a				,					_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= StableDiffusionControlNetInpaintPipeline
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_lowerCAmelCase								= frozenset({'''control_image'''}   )  # skip `image` and `mask` for now, only test for control_image
						_lowerCAmelCase								= TEXT_TO_IMAGE_IMAGE_PARAMS





						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													torch.manual_seed(0			)
													a__:       List[str]											=UNetaDConditionModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       sample_size=3_2      ,       in_channels=9      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,       cross_attention_dim=3_2      ,       )
													torch.manual_seed(0			)
													a__:       Dict											=ControlNetModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       in_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       cross_attention_dim=3_2      ,       conditioning_embedding_out_channels=(1_6, 3_2)      ,       )
													torch.manual_seed(0			)
													a__:       List[Any]											=DDIMScheduler(
													    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="scaled_linear"      ,       clip_sample=_a      ,       set_alpha_to_one=_a      ,       )
													torch.manual_seed(0			)
													a__:       Dict											=AutoencoderKL(
													    block_out_channels=[3_2, 6_4]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       )
													torch.manual_seed(0			)
													a__:       Tuple											=CLIPTextConfig(
													    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=3_2      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )
													a__:       Any											=CLIPTextModel(_a			)
													a__:       Any											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													a__:       str											={
													    "unet": unet,
													    "controlnet": controlnet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components





						def   _lowerCamelCase			( self						:  Any      ,       _a						:  List[Any]      ,       _a						:  Any=0			):
													if str(_a			).startswith("mps"			):
																				a__:       Optional[Any]											=torch.manual_seed(_a			)
													else:
																				a__:       Optional[Any]											=torch.Generator(device=_a			).manual_seed(_a			)

													a__:       int											=2
													a__:       List[Any]											=randn_tensor(
													    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)      ,       generator=_a      ,       device=torch.device(_a			)      ,       )
													a__:       Union[str, Any]											=floats_tensor((1, 3, 3_2, 3_2)      ,       rng=random.Random(_a			)			).to(_a			)
													a__:       Any											=init_image.cpu().permute(0      ,       2      ,       3      ,       1			)[0]

													a__:       List[Any]											=Image.fromarray(np.uinta(_a			)			).convert("RGB"			).resize((6_4, 6_4)			)
													a__:       List[Any]											=Image.fromarray(np.uinta(init_image + 4			)			).convert("RGB"			).resize((6_4, 6_4)			)

													a__:       Optional[Any]											={
													    "prompt": "A painting of a squirrel eating a burger",
													    "generator": generator,
													    "num_inference_steps": 2,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "image": image,
													    "mask_image": mask_image,
													    "control_image": control_image,
													}

													return inputs





						def   _lowerCamelCase			( self						:  Tuple			):
													return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)





						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()      ,       reason="XFormers attention is only available with CUDA and `xformers` installed"      ,       )
						def   _lowerCamelCase			( self						:  Any			):
													self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)







						def   _lowerCamelCase			( self						:  Dict			):
													self._test_inference_batch_single_identical(expected_max_diff=2e-3			)


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= StableDiffusionControlNetInpaintPipeline
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_lowerCAmelCase								= frozenset([]   )





						def   _lowerCamelCase			( self						:  Optional[int]			):
													torch.manual_seed(0			)
													a__:       Tuple											=UNetaDConditionModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       sample_size=3_2      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,       cross_attention_dim=3_2      ,       )
													torch.manual_seed(0			)
													a__:       int											=ControlNetModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       in_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       cross_attention_dim=3_2      ,       conditioning_embedding_out_channels=(1_6, 3_2)      ,       )
													torch.manual_seed(0			)
													a__:       Tuple											=DDIMScheduler(
													    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="scaled_linear"      ,       clip_sample=_a      ,       set_alpha_to_one=_a      ,       )
													torch.manual_seed(0			)
													a__:       Any											=AutoencoderKL(
													    block_out_channels=[3_2, 6_4]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       )
													torch.manual_seed(0			)
													a__:       List[str]											=CLIPTextConfig(
													    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=3_2      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )
													a__:       Dict											=CLIPTextModel(_a			)
													a__:       Optional[int]											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													a__:       Union[str, Any]											={
													    "unet": unet,
													    "controlnet": controlnet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components


class 	lowerCamelCase__ (		_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= StableDiffusionControlNetInpaintPipeline
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						_lowerCAmelCase								= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS





						def   _lowerCamelCase			( self						:  Optional[Any]			):
													torch.manual_seed(0			)
													a__:       Optional[int]											=UNetaDConditionModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       sample_size=3_2      ,       in_channels=9      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,       cross_attention_dim=3_2      ,       )
													torch.manual_seed(0			)

													def init_weights(_a						:  List[Any]			):
																				if isinstance(_a      ,       torch.nn.Convad			):
																											torch.nn.init.normal(m.weight			)
																											m.bias.data.fill_(1.0			)

													a__:       int											=ControlNetModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       in_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       cross_attention_dim=3_2      ,       conditioning_embedding_out_channels=(1_6, 3_2)      ,       )
													controlneta.controlnet_down_blocks.apply(_a			)

													torch.manual_seed(0			)
													a__:       List[str]											=ControlNetModel(
													    block_out_channels=(3_2, 6_4)      ,       layers_per_block=2      ,       in_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       cross_attention_dim=3_2      ,       conditioning_embedding_out_channels=(1_6, 3_2)      ,       )
													controlneta.controlnet_down_blocks.apply(_a			)

													torch.manual_seed(0			)
													a__:       Optional[int]											=DDIMScheduler(
													    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="scaled_linear"      ,       clip_sample=_a      ,       set_alpha_to_one=_a      ,       )
													torch.manual_seed(0			)
													a__:       List[Any]											=AutoencoderKL(
													    block_out_channels=[3_2, 6_4]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       )
													torch.manual_seed(0			)
													a__:       Dict											=CLIPTextConfig(
													    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=3_2      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )
													a__:       Tuple											=CLIPTextModel(_a			)
													a__:       Tuple											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													a__:       Any											=MultiControlNetModel([controlneta, controlneta]			)

													a__:       Tuple											={
													    "unet": unet,
													    "controlnet": controlnet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components





						def   _lowerCamelCase			( self						:  str      ,       _a						:  Union[str, Any]      ,       _a						:  Optional[Any]=0			):
													if str(_a			).startswith("mps"			):
																				a__:       Optional[int]											=torch.manual_seed(_a			)
													else:
																				a__:       Optional[Any]											=torch.Generator(device=_a			).manual_seed(_a			)

													a__:       str											=2

													a__:       Any											=[
													    randn_tensor(
													        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)      ,       generator=_a      ,       device=torch.device(_a			)      ,       ),
													    randn_tensor(
													        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)      ,       generator=_a      ,       device=torch.device(_a			)      ,       ),
													]
													a__:       List[str]											=floats_tensor((1, 3, 3_2, 3_2)      ,       rng=random.Random(_a			)			).to(_a			)
													a__:       List[Any]											=init_image.cpu().permute(0      ,       2      ,       3      ,       1			)[0]

													a__:       Union[str, Any]											=Image.fromarray(np.uinta(_a			)			).convert("RGB"			).resize((6_4, 6_4)			)
													a__:       Optional[Any]											=Image.fromarray(np.uinta(init_image + 4			)			).convert("RGB"			).resize((6_4, 6_4)			)

													a__:       Tuple											={
													    "prompt": "A painting of a squirrel eating a burger",
													    "generator": generator,
													    "num_inference_steps": 2,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "image": image,
													    "mask_image": mask_image,
													    "control_image": control_image,
													}

													return inputs





						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       Tuple											=self.get_dummy_components()
													a__:       List[str]											=self.pipeline_class(**_a			)
													pipe.to(_a			)

													a__:       Any											=1_0.0
													a__:       str											=4

													a__:       List[Any]											=self.get_dummy_inputs(_a			)
													a__:       Optional[int]											=steps
													a__:       List[Any]											=scale
													a__:       Union[str, Any]											=pipe(**_a			)[0]

													a__:       List[str]											=self.get_dummy_inputs(_a			)
													a__:       str											=steps
													a__:       Union[str, Any]											=scale
													a__:       Union[str, Any]											=pipe(**_a      ,       control_guidance_start=0.1      ,       control_guidance_end=0.2			)[0]

													a__:       Tuple											=self.get_dummy_inputs(_a			)
													a__:       Dict											=steps
													a__:       Union[str, Any]											=scale
													a__:       Optional[Any]											=pipe(**_a      ,       control_guidance_start=[0.1, 0.3]      ,       control_guidance_end=[0.2, 0.7]			)[0]

													a__:       Dict											=self.get_dummy_inputs(_a			)
													a__:       int											=steps
													a__:       Tuple											=scale
													a__:       Tuple											=pipe(**_a      ,       control_guidance_start=0.4      ,       control_guidance_end=[0.5, 0.8]			)[0]

													# make sure that all outputs are different
													assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
													assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
													assert np.sum(np.abs(output_a - output_a			)			) > 1e-3





						def   _lowerCamelCase			( self						:  List[str]			):
													return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)





						@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=2e-3			)





						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													self._test_inference_batch_single_identical(expected_max_diff=2e-3			)







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Dict											=self.get_dummy_components()
													a__:       Dict											=self.pipeline_class(**_a			)
													pipe.to(_a			)
													pipe.set_progress_bar_config(disable=_a			)
													with tempfile.TemporaryDirectory() as tmpdir:
																				try:
																											# save_pretrained is not implemented for Multi-ControlNet
																											pipe.save_pretrained(_a			)
																				except NotImplementedError:
																											pass


@slow
@require_torch_gpu
class 	lowerCamelCase__ (		unittest.TestCase   ):





						def   _lowerCamelCase			( self						:  int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   _lowerCamelCase			( self						:  Any			):
													a__:       List[Any]											=ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"			)

													a__:       Any											=StableDiffusionControlNetInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"      ,       safety_checker=_a      ,       controlnet=_a			)
													pipe.enable_model_cpu_offload()
													pipe.set_progress_bar_config(disable=_a			)

													a__:       Any											=torch.Generator(device="cpu"			).manual_seed(0			)
													a__:       List[str]											=load_image(
													    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"			).resize((5_1_2, 5_1_2)			)

													a__:       Any											=load_image(
													    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
													    "/stable_diffusion_inpaint/input_bench_mask.png"			).resize((5_1_2, 5_1_2)			)

													a__:       Optional[int]											="pitch black hole"

													a__:       str											=load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"			).resize((5_1_2, 5_1_2)			)

													a__:       Any											=pipe(
													    _a      ,       image=_a      ,       mask_image=_a      ,       control_image=_a      ,       generator=_a      ,       output_type="np"      ,       num_inference_steps=3      ,       )

													a__:       Optional[Any]											=output.images[0]

													assert image.shape == (5_1_2, 5_1_2, 3)

													a__:       Any											=load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"			)

													assert np.abs(expected_image - image			).max() < 9e-2







						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       Optional[int]											=ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"			)

													a__:       List[str]											=StableDiffusionControlNetInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"      ,       safety_checker=_a      ,       controlnet=_a			)
													a__:       Dict											=DDIMScheduler.from_config(pipe.scheduler.config			)
													pipe.enable_model_cpu_offload()
													pipe.set_progress_bar_config(disable=_a			)

													a__:       Optional[Any]											=torch.Generator(device="cpu"			).manual_seed(3_3			)

													a__:       Optional[Any]											=load_image(
													    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"			)
													a__:       Dict											=init_image.resize((5_1_2, 5_1_2)			)

													a__:       Any											=load_image(
													    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"			)
													a__:       int											=mask_image.resize((5_1_2, 5_1_2)			)

													a__:       Union[str, Any]											="a handsome man with ray-ban sunglasses"

													def make_inpaint_condition(_a						:  List[Any]      ,       _a						:  Dict			):
																				a__:       int											=np.array(image.convert("RGB"			)			).astype(np.floataa			) / 2_5_5.0
																				a__:       int											=np.array(image_mask.convert("L"			)			).astype(np.floataa			) / 2_5_5.0

																				assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
																				a__:       Any											=-1.0  # set as masked pixel
																				a__:       List[Any]											=np.expand_dims(_a      ,       0			).transpose(0      ,       3      ,       1      ,       2			)
																				a__:       Optional[Any]											=torch.from_numpy(_a			)
																				return image

													a__:       List[Any]											=make_inpaint_condition(_a      ,       _a			)

													a__:       Tuple											=pipe(
													    _a      ,       image=_a      ,       mask_image=_a      ,       control_image=_a      ,       guidance_scale=9.0      ,       eta=1.0      ,       generator=_a      ,       num_inference_steps=2_0      ,       output_type="np"      ,       )
													a__:       Union[str, Any]											=output.images[0]

													assert image.shape == (5_1_2, 5_1_2, 3)

													a__:       Tuple											=load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"			)

													assert np.abs(expected_image - image			).max() < 9e-2










from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__UpperCAmelCase						  =     logging.get_logger(__name__)  # pylint: disable=invalid-name


__UpperCAmelCase						  =     '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''


class 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  str      ,       _a						:  UNetaDModel      ,       _a						:  CMStochasticIterativeScheduler			):
													super().__init__()

													self.register_modules(
													    unet=_a      ,       scheduler=_a      ,       )

													a__:       Union[str, Any]											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Any=0			):
													if is_accelerate_available() and is_accelerate_version(">="      ,       "0.14.0"			):
																				from accelerate import cpu_offload
													else:
																				raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"			)

													a__:       Optional[Any]											=torch.device(F"cuda:{gpu_id}"			)

													if self.device.type != "cpu":
																				self.to("cpu"      ,       silence_dtype_warnings=_a			)
																				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													for cpu_offloaded_model in [self.unet]:
																				cpu_offload(_a      ,       _a			)

													if self.safety_checker is not None:
																				cpu_offload(self.safety_checker      ,       execution_device=_a      ,       offload_buffers=_a			)


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Optional[int]=0			):
													if is_accelerate_available() and is_accelerate_version(">="      ,       "0.17.0.dev0"			):
																				from accelerate import cpu_offload_with_hook
													else:
																				raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."			)

													a__:       Union[str, Any]											=torch.device(F"cuda:{gpu_id}"			)

													if self.device.type != "cpu":
																				self.to("cpu"      ,       silence_dtype_warnings=_a			)
																				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													a__:       Optional[Any]											=None
													for cpu_offloaded_model in [self.unet]:
																				a__							,			a__:       Optional[Any]											=cpu_offload_with_hook(_a      ,       _a      ,       prev_module_hook=_a			)

													if self.safety_checker is not None:
																				a__							,			a__:       Optional[Any]											=cpu_offload_with_hook(self.safety_checker      ,       _a      ,       prev_module_hook=_a			)

													# We'll offload the last model manually.
													a__:       Optional[Any]											=hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   _lowerCamelCase			( self						:  Tuple			):
													if not hasattr(self.unet      ,       "_hf_hook"			):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(_a      ,       "_hf_hook"			)
																				    and hasattr(module._hf_hook      ,       "execution_device"			)
																				    and module._hf_hook.execution_device is not None
																				):
																											return torch.device(module._hf_hook.execution_device			)
													return self.device


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  List[Any]      ,       _a						:  Optional[int]      ,       _a						:  Dict      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  int=None			):
													a__:       List[str]											=(batch_size, num_channels, height, width)
													if isinstance(_a      ,       _a			) and len(_a			) != batch_size:
																				raise ValueError(
																				    F"You have passed a list of generators of length {len(_a			)}, but requested an effective batch"
																				    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													if latents is None:
																				a__:       Dict											=randn_tensor(_a      ,       generator=_a      ,       device=_a      ,       dtype=_a			)
													else:
																				a__:       List[Any]											=latents.to(device=_a      ,       dtype=_a			)

													# scale the initial noise by the standard deviation required by the scheduler
													a__:       int											=latents * self.scheduler.init_noise_sigma
													return latents


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  torch.FloatTensor      ,       _a						:  str = "pil"			):
													if output_type not in ["pt", "np", "pil"]:
																				raise ValueError(
																				    F"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"			)

													# Equivalent to diffusers.VaeImageProcessor.denormalize
													a__:       Tuple											=(sample / 2 + 0.5).clamp(0      ,       1			)
													if output_type == "pt":
																				return sample

													# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
													a__:       Tuple											=sample.cpu().permute(0      ,       2      ,       3      ,       1			).numpy()
													if output_type == "np":
																				return sample

													# Output_type must be 'pil'
													a__:       List[Any]											=self.numpy_to_pil(_a			)
													return sample


						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  Dict      ,       _a						:  Optional[Any]      ,       _a						:  List[str]=None			):
													if self.unet.config.num_class_embeds is not None:
																				if isinstance(_a      ,       _a			):
																											a__:       List[str]											=torch.tensor(_a      ,       dtype=torch.int			)
																				elif isinstance(_a      ,       _a			):
																											assert batch_size == 1, "Batch size must be 1 if classes is an int"
																											a__:       List[str]											=torch.tensor([class_labels]      ,       dtype=torch.int			)
																				elif class_labels is None:
																											# Randomly generate batch_size class labels
																											# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																											a__:       Union[str, Any]											=torch.randint(0      ,       self.unet.config.num_class_embeds      ,       size=(batch_size,)			)
																				a__:       Dict											=class_labels.to(_a			)
													else:
																				a__:       List[str]											=None
													return class_labels


						def   _lowerCamelCase			( self						:  Union[str, Any]      ,       _a						:  Optional[Any]      ,       _a						:  Optional[int]      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Optional[int]      ,       _a						:  Optional[Any]			):
													if num_inference_steps is None and timesteps is None:
																				raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."			)

													if num_inference_steps is not None and timesteps is not None:
																				logger.warning(
																				    F"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
																				    " `timesteps` will be used over `num_inference_steps`."			)

													if latents is not None:
																				a__:       Tuple											=(batch_size, 3, img_size, img_size)
																				if latents.shape != expected_shape:
																											raise ValueError(F"The shape of latents is {latents.shape} but is expected to be {expected_shape}."			)

													if (callback_steps is None) or (
													    callback_steps is not None and (not isinstance(_a      ,       _a			) or callback_steps <= 0)
													):
																				raise ValueError(
																				    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
																				    F" {type(_a			)}."			)







						@torch.no_grad()
						@replace_example_docstring(_a			)
						def __call__( self						:  Dict      ,       _a						:  int = 1      ,       _a						:  Optional[Union[torch.Tensor, List[int], int]] = None      ,       _a						:  int = 1      ,       _a						:  List[int] = None      ,       _a						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       _a						:  Optional[torch.FloatTensor] = None      ,       _a						:  Optional[str] = "pil"      ,       _a						:  bool = True      ,       _a						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       _a						:  int = 1      ,       ):
													a__:       int											=self.unet.config.sample_size
													a__:       Union[str, Any]											=self._execution_device

													# 1. Check inputs
													self.check_inputs(_a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a			)

													# 2. Prepare image latents
													# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
													a__:       Tuple											=self.prepare_latents(
													    batch_size=_a      ,       num_channels=self.unet.config.in_channels      ,       height=_a      ,       width=_a      ,       dtype=self.unet.dtype      ,       device=_a      ,       generator=_a      ,       latents=_a      ,       )

													# 3. Handle class_labels for class-conditional models
													a__:       int											=self.prepare_class_labels(_a      ,       _a      ,       class_labels=_a			)

													# 4. Prepare timesteps
													if timesteps is not None:
																				self.scheduler.set_timesteps(timesteps=_a      ,       device=_a			)
																				a__:       str											=self.scheduler.timesteps
																				a__:       List[str]											=len(_a			)
													else:
																				self.scheduler.set_timesteps(_a			)
																				a__:       Optional[int]											=self.scheduler.timesteps

													# 5. Denoising loop
													# Multistep sampling: implements Algorithm 1 in the paper
													with self.progress_bar(total=_a			) as progress_bar:
																				for i, t in enumerate(_a			):
																											a__:       Any											=self.scheduler.scale_model_input(_a      ,       _a			)
																											a__:       Dict											=self.unet(_a      ,       _a      ,       class_labels=_a      ,       return_dict=_a			)[0]

																											a__:       int											=self.scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a			)[0]

																											# call the callback, if provided
																											progress_bar.update()
																											if callback is not None and i % callback_steps == 0:
																																		callback(_a      ,       _a      ,       _a			)

        # 6. Post-process image sample
													a__:       List[str]											=self.postprocess_image(_a      ,       output_type=_a			)

													# Offload last model to CPU
													if hasattr(self      ,       "final_offload_hook"			) and self.final_offload_hook is not None:
																				self.final_offload_hook.offload()

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=_a			)





