


import unittest
import warnings

from datasets.utils import experimental

@experimental
def 			__SCREAMING_SNAKE_CASE		(		)     ->	Union[str, Any]:



	"""simple docstring"""






	return "success"

class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								with warnings.catch_warnings(record=_SCREAMING_SNAKE_CASE    ) as w:
									warnings.simplefilter("""always"""    )
									self.assertEqual(dummy_function()			,       """success"""    )
								self.assertEqual(len(_SCREAMING_SNAKE_CASE    )			,       1    )



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    )