


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE__						(       lowercase_    ):
      snake_case__       :  int       =		(DEISMultistepScheduler,)
      snake_case__       :  List[str]       =		(('''num_inference_steps''', 25),)
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							**SCREAMING_SNAKE_CASE__       : Any  )	->    Union[str, Any]:
             a_      :	Dict							=						{
                 """num_train_timesteps""": 1_0_0_0,
                 """beta_start""": 0.0001,
                 """beta_end""": 0.02,
                 """beta_schedule""": """linear""",
                 """solver_order""": 2,
             }

             config.update(**lowerCamelCase_  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any=0  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    Any:
             a_      :	Any							=						dict(self.forward_default_kwargs  )
             a_      :	List[str]							=						kwargs.pop('num_inference_steps'  ,							lowerCamelCase_  )
             a_      :	Dict							=						self.dummy_sample
             a_      :	Any							=						0.1 * sample
             a_      :	str							=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowerCamelCase_  )
                           a_      :	Optional[int]							=						scheduler_class.from_pretrained(lowerCamelCase_  )
                           new_scheduler.set_timesteps(lowerCamelCase_  )
                           # copy over dummy past residuals
                           a_      :	List[str]							=						dummy_past_residuals[: new_scheduler.config.solver_order]

                    a_      :	str							=						sample, sample
                    for t in range(lowerCamelCase_  ,							time_step + scheduler.config.solver_order + 1  ):
                           a_      :	Tuple							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample
                           a_      :	Optional[int]							=						new_scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample

                           assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Union[str, Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0  ,							**SCREAMING_SNAKE_CASE__       : Any  )	->    Any:
             a_      :	Tuple							=						dict(self.forward_default_kwargs  )
             a_      :	Optional[Any]							=						kwargs.pop('num_inference_steps'  ,							lowerCamelCase_  )
             a_      :	Optional[int]							=						self.dummy_sample
             a_      :	Optional[Any]							=						0.1 * sample
             a_      :	Optional[Any]							=						[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    a_      :	str							=						self.get_scheduler_config()
                    a_      :	Any							=						scheduler_class(**lowerCamelCase_  )
                    scheduler.set_timesteps(lowerCamelCase_  )

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

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

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

                    a_      :	List[Any]							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample
                    a_      :	Tuple							=						new_scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample

                    assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							**SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Any:



             if scheduler is None:
                    a_      :	Optional[int]							=						self.scheduler_classes[0]
                    a_      :	Optional[Any]							=						self.get_scheduler_config(**lowerCamelCase_  )
                    a_      :	int							=						scheduler_class(**lowerCamelCase_  )

             a_      :	List[Any]							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**lowerCamelCase_  )
             a_      :	str							=						scheduler_class(**lowerCamelCase_  )

             a_      :	Tuple							=						1_0
             a_      :	Any							=						self.dummy_model()
             a_      :	Union[str, Any]							=						self.dummy_sample_deter
             scheduler.set_timesteps(lowerCamelCase_  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	Dict							=						model(lowerCamelCase_  ,							lowerCamelCase_  )
                    a_      :	Optional[int]							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[str]:
             a_      :	Tuple							=						dict(self.forward_default_kwargs  )

             a_      :	int							=						kwargs.pop('num_inference_steps'  ,							lowerCamelCase_  )

             for scheduler_class in self.scheduler_classes:
                    a_      :	str							=						self.get_scheduler_config()
                    a_      :	Optional[Any]							=						scheduler_class(**lowerCamelCase_  )

                    a_      :	Optional[int]							=						self.dummy_sample
                    a_      :	Any							=						0.1 * sample

                    if num_inference_steps is not None and hasattr(lowerCamelCase_  ,							'set_timesteps'  ):
                           scheduler.set_timesteps(lowerCamelCase_  )
                    elif num_inference_steps is not None and not hasattr(lowerCamelCase_  ,							'set_timesteps'  ):
                           a_      :	str							=						num_inference_steps

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

                    a_      :	int							=						scheduler.timesteps[5]
                    a_      :	int							=						scheduler.timesteps[6]

                    a_      :	List[Any]							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample
                    a_      :	Tuple							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							**lowerCamelCase_  ).prev_sample

                    self.assertEqual(output_a.shape  ,							sample.shape  )
                    self.assertEqual(output_a.shape  ,							output_a.shape  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[str]:
             a_      :	str							=						DEISMultistepScheduler(**self.get_scheduler_config()  )
             a_      :	Union[str, Any]							=						self.full_loop(scheduler=lowerCamelCase_  )
             a_      :	List[Any]							=						torch.mean(torch.abs(lowerCamelCase_  )  )

             assert abs(result_mean.item() - 0.23916  ) < 1E-3

             a_      :	List[str]							=						DPMSolverSinglestepScheduler.from_config(scheduler.config  )
             a_      :	Tuple							=						DPMSolverMultistepScheduler.from_config(scheduler.config  )
             a_      :	Dict							=						UniPCMultistepScheduler.from_config(scheduler.config  )
             a_      :	int							=						DEISMultistepScheduler.from_config(scheduler.config  )

             a_      :	Optional[Any]							=						self.full_loop(scheduler=lowerCamelCase_  )
             a_      :	List[str]							=						torch.mean(torch.abs(lowerCamelCase_  )  )

             assert abs(result_mean.item() - 0.23916  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:



             for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=lowerCamelCase_  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             self.check_over_configs(thresholding=lowerCamelCase_  )
             for order in [1, 2, 3]:
                    for solver_type in ["logrho"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=lowerCamelCase_  ,							prediction_type=lowerCamelCase_  ,							sample_max_value=lowerCamelCase_  ,							algorithm_type='deis'  ,							solver_order=lowerCamelCase_  ,							solver_type=lowerCamelCase_  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Optional[int]:



             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowerCamelCase_  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Dict:



             for algorithm_type in ["deis"]:
                    for solver_type in ["logrho"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=lowerCamelCase_  ,							solver_type=lowerCamelCase_  ,							prediction_type=lowerCamelCase_  ,							algorithm_type=lowerCamelCase_  ,							)
                                         a_      :	Dict							=						self.full_loop(
                                             solver_order=lowerCamelCase_  ,							solver_type=lowerCamelCase_  ,							prediction_type=lowerCamelCase_  ,							algorithm_type=lowerCamelCase_  ,							)
                                         assert not torch.isnan(lowerCamelCase_  ).any(), "Samples have nan numbers"
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             self.check_over_configs(lower_order_final=lowerCamelCase_  )
             self.check_over_configs(lower_order_final=lowerCamelCase_  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[int]:



             for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                    self.check_over_forward(num_inference_steps=lowerCamelCase_  ,							time_step=0  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Union[str, Any]:
             a_      :	List[Any]							=						self.full_loop()
             a_      :	List[Any]							=						torch.mean(torch.abs(lowerCamelCase_  )  )

             assert abs(result_mean.item() - 0.23916  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    str:
             a_      :	List[str]							=						self.full_loop(prediction_type='v_prediction'  )
             a_      :	Any							=						torch.mean(torch.abs(lowerCamelCase_  )  )

             assert abs(result_mean.item() - 0.091  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
             a_      :	Optional[int]							=						self.scheduler_classes[0]
             a_      :	List[str]							=						self.get_scheduler_config(thresholding=lowerCamelCase_  ,							dynamic_thresholding_ratio=0  )
             a_      :	Dict							=						scheduler_class(**lowerCamelCase_  )

             a_      :	List[str]							=						1_0
             a_      :	Optional[int]							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter.half()
             scheduler.set_timesteps(lowerCamelCase_  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	List[Any]							=						model(lowerCamelCase_  ,							lowerCamelCase_  )
                    a_      :	List[Any]							=						scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ).prev_sample

             assert sample.dtype == torch.floataa






from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase_   :				str						      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    Tuple=8       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[int]							=						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_      :	int							=						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : DDPMScheduler  ,							SCREAMING_SNAKE_CASE__       : VQModel  ,							)	->    Any:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							movq=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             if latents is None:
                    a_      :	List[str]							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  ,							dtype=SCREAMING_SNAKE_CASE__  )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
                    a_      :	Any							=						latents.to(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						latents * scheduler.init_noise_sigma
             return latents
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0  )	->    Dict:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'  )

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

             a_      :	Optional[Any]							=						[
                 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  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  )	->    Dict:
             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_      :	Tuple							=						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_      :	Dict							=						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,	a_      :	Optional[Any]							=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							prev_module_hook=SCREAMING_SNAKE_CASE__  )

             # We'll offload the last model manually.
             a_      :	str							=						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             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       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : torch.FloatTensor  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 4.0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    int:
             a_      :	Dict							=						self._execution_device

             a_      :	int							=						guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Union[str, Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	Dict							=						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_      :	Optional[Any]							=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	Optional[Any]							=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	int							=						hint.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	int							=						torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						torch.cat([hint, hint]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.scheduler.timesteps

             a_      :	Optional[int]							=						self.movq.config.latent_channels

             a_							,	a_      :	Dict							=						downscale_height_and_width(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.movq_scale_factor  )

             # create initial latent
             a_      :	int							=						self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)  ,							image_embeds.dtype  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.scheduler  ,							)

             for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__  )  ):
                    # expand the latents if we are doing classifier free guidance
                    a_      :	int							=						torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

                    a_      :	int							=						{'image_embeds': image_embeds, 'hint': hint}
                    a_      :	str							=						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_      :	Optional[int]							=						noise_pred.split(latents.shape[1]  ,							dim=1  )
                           a_							,	a_      :	str							=						noise_pred.chunk(2  )
                           a_							,	a_      :	Union[str, Any]							=						variance_pred.chunk(2  )
                           a_      :	Optional[int]							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           a_      :	str							=						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_      :	Optional[int]							=						noise_pred.split(latents.shape[1]  ,							dim=1  )

                    # compute the previous noisy sample x_t -> x_t-1
                    a_      :	str							=						self.scheduler.step(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							)[0]
             # post-processing
             a_      :	str							=						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_      :	str							=						image * 0.5 + 0.5
                    a_      :	Union[str, Any]							=						image.clamp(0  ,							1  )
                    a_      :	Optional[int]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

             if output_type == "pil":
                    a_      :	List[str]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )


