
"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _SCREAMING_SNAKE_CASE):
    __lowercase				:			Optional[int]	=       (DPMSolverMultistepScheduler,)
    __lowercase				:			Optional[Any]	=       (("""num_inference_steps""", 2_5),)
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Union[str, Any]            = {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "solver_order": 2,
          "prediction_type": "epsilon",
          "thresholding": False,
          "sample_max_value": 1.0,
          "algorithm_type": "dpmsolver++",
          "solver_type": "midpoint",
          "lower_order_final": False,
          "lambda_min_clipped": -float("inf"  ),
          "variance_type": None,
      }

      config.update(**_SCREAMING_SNAKE_CASE  )
      return config
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    List[str]            = dict(self.forward_default_kwargs  )
      _snake_case  :    List[Any]            = kwargs.pop("num_inference_steps"    ,     _SCREAMING_SNAKE_CASE  )
      _snake_case  :    List[Any]            = self.dummy_sample
      _snake_case  :    Dict            = 0.1 * sample
      _snake_case  :    List[str]            = [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    List[Any]            = self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
        _snake_case  :    Optional[Any]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
        # copy over dummy past residuals
        _snake_case  :    List[Any]            = dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(_SCREAMING_SNAKE_CASE  )
          _snake_case  :    List[Any]            = scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
          new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
          # copy over dummy past residuals
          _snake_case  :    Dict            = dummy_past_residuals[: new_scheduler.config.solver_order]

        _snake_case      ,       _snake_case  :    Optional[int]            = sample, sample
        for t in range(_SCREAMING_SNAKE_CASE    ,     time_step + scheduler.config.solver_order + 1  ):
          _snake_case  :    Union[str, Any]            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample
          _snake_case  :    Optional[int]            = new_scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample

          assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    Union[str, Any]            = dict(self.forward_default_kwargs  )
      _snake_case  :    Optional[Any]            = kwargs.pop("num_inference_steps"    ,     _SCREAMING_SNAKE_CASE  )
      _snake_case  :    Optional[Any]            = self.dummy_sample
      _snake_case  :    Any            = 0.1 * sample
      _snake_case  :    List[Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    int            = self.get_scheduler_config()
        _snake_case  :    List[Any]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

        # copy over dummy past residuals (must be after setting timesteps)
        _snake_case  :    List[str]            = dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(_SCREAMING_SNAKE_CASE  )
          _snake_case  :    List[str]            = scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
          # copy over dummy past residuals
          new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

          # copy over dummy past residual (must be after setting timesteps)
          _snake_case  :    Optional[int]            = dummy_past_residuals[: new_scheduler.config.solver_order]

        _snake_case  :    List[Any]            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample
        _snake_case  :    Union[str, Any]            = new_scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
    def        lowerCamelCase__						(     self    ,     snake_case_=None    ,     **snake_case_  ):
      if scheduler is None:
        _snake_case  :    Optional[Any]            = self.scheduler_classes[0]
        _snake_case  :    Dict            = self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
        _snake_case  :    Optional[int]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )

      _snake_case  :    Optional[int]            = 10
      _snake_case  :    Tuple            = self.dummy_model()
      _snake_case  :    Any            = self.dummy_sample_deter
      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    Union[str, Any]            = model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE  )
        _snake_case  :    str            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE  ).prev_sample

      return sample
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = dict(self.forward_default_kwargs  )

      _snake_case  :    str            = kwargs.pop("num_inference_steps"    ,     _SCREAMING_SNAKE_CASE  )

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Optional[int]            = self.get_scheduler_config()
        _snake_case  :    Optional[Any]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )

        _snake_case  :    List[str]            = self.dummy_sample
        _snake_case  :    Tuple            = 0.1 * sample

        if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,     "set_timesteps"  ):
          scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
        elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,     "set_timesteps"  ):
          _snake_case  :    Dict            = num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        _snake_case  :    List[Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]
        _snake_case  :    Any            = dummy_past_residuals[: scheduler.config.solver_order]

        _snake_case  :    Union[str, Any]            = scheduler.timesteps[5]
        _snake_case  :    Tuple            = scheduler.timesteps[6]

        _snake_case  :    Dict            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample
        _snake_case  :    Dict            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE  ).prev_sample

        self.assertEqual(output_a.shape    ,     sample.shape  )
        self.assertEqual(output_a.shape    ,     output_a.shape  )
    def        lowerCamelCase__						(     self  ):
      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE  )
      for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
          for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE    ,     prediction_type=_SCREAMING_SNAKE_CASE    ,     sample_max_value=_SCREAMING_SNAKE_CASE    ,     algorithm_type="dpmsolver++"    ,     solver_order=_SCREAMING_SNAKE_CASE    ,     solver_type=_SCREAMING_SNAKE_CASE    ,     )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE  )
    def        lowerCamelCase__						(     self  ):
      for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
          for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
              if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                if order == 3:
                  continue
              else:
                self.check_over_configs(
                    solver_order=_SCREAMING_SNAKE_CASE    ,     solver_type=_SCREAMING_SNAKE_CASE    ,     prediction_type=_SCREAMING_SNAKE_CASE    ,     algorithm_type=_SCREAMING_SNAKE_CASE    ,     )
              _snake_case  :    Union[str, Any]            = self.full_loop(
                  solver_order=_SCREAMING_SNAKE_CASE    ,     solver_type=_SCREAMING_SNAKE_CASE    ,     prediction_type=_SCREAMING_SNAKE_CASE    ,     algorithm_type=_SCREAMING_SNAKE_CASE    ,     )
              assert not torch.isnan(_SCREAMING_SNAKE_CASE  ).any(), "Samples have nan numbers"
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE  )
      self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
      self.check_over_configs(lambda_min_clipped=-5.1  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE  )
      self.check_over_configs(variance_type="learned_range"  )
    def        lowerCamelCase__						(     self  ):
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE    ,     time_step=0  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = self.full_loop()
      _snake_case  :    List[Any]            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.full_loop(thresholding=_SCREAMING_SNAKE_CASE    ,     dynamic_thresholding_ratio=0.87    ,     sample_max_value=0.5  )
      _snake_case  :    str            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 1.1364  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.full_loop(prediction_type="v_prediction"  )
      _snake_case  :    Optional[Any]            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 0.2251  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = self.full_loop(prediction_type="v_prediction"    ,     use_karras_sigmas=_SCREAMING_SNAKE_CASE  )
      _snake_case  :    List[str]            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 0.2096  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _snake_case  :    Optional[int]            = DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
      _snake_case  :    List[Any]            = self.full_loop(scheduler=_SCREAMING_SNAKE_CASE  )
      _snake_case  :    Any            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3

      _snake_case  :    Any            = DPMSolverSinglestepScheduler.from_config(scheduler.config  )
      _snake_case  :    List[Any]            = UniPCMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    Optional[Any]            = DEISMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    Tuple            = DPMSolverMultistepScheduler.from_config(scheduler.config  )

      _snake_case  :    Dict            = self.full_loop(scheduler=_SCREAMING_SNAKE_CASE  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.scheduler_classes[0]
      _snake_case  :    Any            = self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE    ,     dynamic_thresholding_ratio=0  )
      _snake_case  :    Union[str, Any]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )

      _snake_case  :    Tuple            = 10
      _snake_case  :    str            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter.half()
      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    Optional[int]            = model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE  )
        _snake_case  :    List[str]            = scheduler.step(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE  ).prev_sample

      assert sample.dtype == torch.floataa


    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      for scheduler_class in self.scheduler_classes:
        _snake_case  :    List[str]            = self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
        _snake_case  :    List[Any]            = scheduler_class(**_SCREAMING_SNAKE_CASE  )

        scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
        assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_A							     =       '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      str=8	)     ->	Any:



	"""simple docstring"""






	a_     			=   height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	a_     			=   width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Dict=512     ,					UpperCamelCase		:      Optional[Any]=512	)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   pil_image.resize((w, h)     ,					resample=Image.BICUBIC     ,					reducing_gap=1	)
	a_     			=   np.array(pil_image.convert("""RGB"""	)	)
	a_     			=   arr.astype(np.floataa	) / 127.5 - 1
	a_     			=   np.transpose(UpperCamelCase     ,					[2, 0, 1]	)
	a_     			=   torch.from_numpy(UpperCamelCase	).unsqueeze(0	)
	return image

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       ):
								super().__init__()

								self.register_modules(
								    unet=_SCREAMING_SNAKE_CASE			,       scheduler=_SCREAMING_SNAKE_CASE			,       movq=_SCREAMING_SNAKE_CASE			,       )
								a_     			=   2 ** (len(self.movq.config.block_out_channels    ) - 1)





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								# get the original timestep using init_timestep
								a_     			=   min(int(num_inference_steps * strength    )			,       _SCREAMING_SNAKE_CASE    )

								a_     			=   max(num_inference_steps - init_timestep			,       0    )
								a_     			=   self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None    ):
								if not isinstance(_SCREAMING_SNAKE_CASE			,       (torch.Tensor, PIL.Image.Image, list)    ):
									raise ValueError(
									    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE    )}"""    )

								a_     			=   image.to(device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								a_     			=   batch_size * num_images_per_prompt

								if image.shape[1] == 4:
									a_     			=   image

								else:
									if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ) and len(_SCREAMING_SNAKE_CASE    ) != batch_size:
										raise ValueError(
										    f"""You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE    )}, but requested an effective batch"""
										    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

									elif isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
										a_     			=   [
										    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_SCREAMING_SNAKE_CASE    )
										]
										a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
									else:
										a_     			=   self.movq.encode(_SCREAMING_SNAKE_CASE    ).latent_dist.sample(_SCREAMING_SNAKE_CASE    )

									a_     			=   self.movq.config.scaling_factor * init_latents

								a_     			=   torch.cat([init_latents]			,       dim=0    )

								a_     			=   init_latents.shape
								a_     			=   randn_tensor(_SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								# get latents
								a_     			=   self.scheduler.add_noise(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								a_     			=   init_latents

								return latents





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0    ):
								if is_accelerate_available():
									from accelerate import cpu_offload
								else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

								a_     			=   torch.device(f"""cuda:{gpu_id}"""    )

								a_     			=   [
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
										cpu_offload(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=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_     			=   torch.device(f"""cuda:{gpu_id}"""    )

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

								a_     			=   None
								for cpu_offloaded_model in [self.unet, self.movq]:
									a_			, a_     			=   cpu_offload_with_hook(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       prev_module_hook=_SCREAMING_SNAKE_CASE    )

								# We'll offload the last model manually.
								a_     			=   hook





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   __magic_name__      (							self    ):
								if not hasattr(self.unet			,       """_hf_hook"""    ):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(_SCREAMING_SNAKE_CASE			,       """_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





							@torch.no_grad()
							@replace_example_docstring(_SCREAMING_SNAKE_CASE    )
							def __call__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 100			,       _SCREAMING_SNAKE_CASE = 4.0			,       _SCREAMING_SNAKE_CASE = 0.3			,       _SCREAMING_SNAKE_CASE = 1			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = "pil"			,       _SCREAMING_SNAKE_CASE = True			,       ):
								a_     			=   self._execution_device

								a_     			=   guidance_scale > 1.0

								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )

								a_     			=   image_embeds.shape[0]

								if do_classifier_free_guidance:
									a_     			=   image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
									a_     			=   negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
									a_     			=   hint.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )

								a_     			=   torch.cat([negative_image_embeds, image_embeds]			,       dim=0    ).to(dtype=self.unet.dtype			,       device=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.cat([hint, hint]			,       dim=0    ).to(dtype=self.unet.dtype			,       device=_SCREAMING_SNAKE_CASE    )

								if not isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   [image]
								if not all(isinstance(_SCREAMING_SNAKE_CASE			,       (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
									raise ValueError(
									    f"""Input is in incorrect format: {[type(_SCREAMING_SNAKE_CASE    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

								a_     			=   torch.cat([prepare_image(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ) for i in image]			,       dim=0    )
								a_     			=   image.to(dtype=image_embeds.dtype			,       device=_SCREAMING_SNAKE_CASE    )

								a_     			=   self.movq.encode(_SCREAMING_SNAKE_CASE    )["""latents"""]
								a_     			=   latents.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE    )
								a_			, a_     			=   self.get_timesteps(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   timesteps[:1].repeat(batch_size * num_images_per_prompt    )
								a_			, a_     			=   downscale_height_and_width(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       self.movq_scale_factor    )
								a_     			=   self.prepare_latents(
								    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       image_embeds.dtype			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE    )    ):
									# expand the latents if we are doing classifier free guidance
									a_     			=   torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

									a_     			=   {"""image_embeds""": image_embeds, """hint""": hint}
									a_     			=   self.unet(
									    sample=_SCREAMING_SNAKE_CASE			,       timestep=_SCREAMING_SNAKE_CASE			,       encoder_hidden_states=_SCREAMING_SNAKE_CASE			,       added_cond_kwargs=_SCREAMING_SNAKE_CASE			,       return_dict=_SCREAMING_SNAKE_CASE			,       )[0]

									if do_classifier_free_guidance:
										a_			, a_     			=   noise_pred.split(latents.shape[1]			,       dim=1    )
										a_			, a_     			=   noise_pred.chunk(2    )
										a_			, a_     			=   variance_pred.chunk(2    )
										a_     			=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										a_     			=   torch.cat([noise_pred, variance_pred_text]			,       dim=1    )

									if not (
									    hasattr(self.scheduler.config			,       """variance_type"""    )
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
										a_			, a_     			=   noise_pred.split(latents.shape[1]			,       dim=1    )

									# compute the previous noisy sample x_t -> x_t-1

									a_     			=   self.scheduler.step(
									    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       )[0]

								# post-processing
								a_     			=   self.movq.decode(_SCREAMING_SNAKE_CASE			,       force_not_quantize=_SCREAMING_SNAKE_CASE    )["""sample"""]

								if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

								if output_type in ["np", "pil"]:
									a_     			=   image * 0.5 + 0.5
									a_     			=   image.clamp(0			,       1    )
									a_     			=   image.cpu().permute(0			,       2			,       3			,       1    ).float().numpy()

								if output_type == "pil":
									a_     			=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE    )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )