






"""simple docstring"""



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,
)


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

__A		:       int  				=		'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: int		,					__snake_case				: Dict=8	):
		lowercase_ : List[str]							       =   height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase_ : int							       =   width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






class 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						List[Any]    ,						A     :						UNetaDConditionModel    ,						A     :						DDPMScheduler    ,						A     :						VQModel    ,						)  ->  Any:
							super().__init__()

							self.register_modules(
							    unet=A    ,						scheduler=A    ,						movq=A    ,						)
							lowercase_ : List[str]							       =   2 ** (len(self.movq.config.block_out_channels   ) - 1)




					def   A  (				self     :						Optional[Any]    ,						A     :						Optional[Any]    ,						A     :						Any    ,						A     :						List[str]    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						List[str]   )  ->  List[str]:
							if latents is None:
									lowercase_ : str							       =   randn_tensor(A    ,						generator=A    ,						device=A    ,						dtype=A   )
							else:
									if latents.shape != shape:
											raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''   )
									lowercase_ : List[Any]							       =   latents.to(A   )

							lowercase_ : Union[str, Any]							       =   latents * scheduler.init_noise_sigma
							return latents




					def   A  (				self     :						Optional[Any]    ,						A     :						Union[str, Any]=0   )  ->  int:
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

							lowercase_ : str							       =   torch.device(F'''cuda:{gpu_id}'''   )

							lowercase_ : List[Any]							       =   [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(A    ,						A   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]=0   )  ->  int:
							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.'''   )

							lowercase_ : List[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)

							lowercase_ : Union[str, Any]							       =   None
							for cpu_offloaded_model in [self.unet, self.movq]:
									lowercase_       ,					lowercase_ : Any							       =   cpu_offload_with_hook(A    ,						A    ,						prev_module_hook=A   )

							# We'll offload the last model manually.
							lowercase_ : Dict							       =   hook




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   A  (				self     :						str   )  ->  str:
							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






					@torch.no_grad()
					@replace_example_docstring(A   )
					def __call__(				self     :						List[Any]    ,						A     :						Union[torch.FloatTensor, List[torch.FloatTensor]]    ,						A     :						Union[torch.FloatTensor, List[torch.FloatTensor]]    ,						A     :						torch.FloatTensor    ,						A     :						int = 5_12    ,						A     :						int = 5_12    ,						A     :						int = 1_00    ,						A     :						float = 4.0    ,						A     :						int = 1    ,						A     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						A     :						Optional[torch.FloatTensor] = None    ,						A     :						Optional[str] = "pil"    ,						A     :						bool = True    ,						)  ->  List[Any]:
							lowercase_ : List[str]							       =   self._execution_device

							lowercase_ : Optional[Any]							       =   guidance_scale > 1.0

							if isinstance(A    ,						A   ):
									lowercase_ : str							       =   torch.cat(A    ,						dim=0   )
							if isinstance(A    ,						A   ):
									lowercase_ : Optional[int]							       =   torch.cat(A    ,						dim=0   )
							if isinstance(A    ,						A   ):
									lowercase_ : Optional[Any]							       =   torch.cat(A    ,						dim=0   )

							lowercase_ : List[str]							       =   image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
									lowercase_ : Any							       =   image_embeds.repeat_interleave(A    ,						dim=0   )
									lowercase_ : Tuple							       =   negative_image_embeds.repeat_interleave(A    ,						dim=0   )
									lowercase_ : Optional[int]							       =   hint.repeat_interleave(A    ,						dim=0   )

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

							lowercase_ : Union[str, Any]							       =   self.movq.config.latent_channels

							lowercase_       ,					lowercase_ : Any							       =   downscale_height_and_width(A    ,						A    ,						self.movq_scale_factor   )

							# create initial latent
							lowercase_ : Dict							       =   self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)    ,						image_embeds.dtype    ,						A    ,						A    ,						A    ,						self.scheduler    ,						)

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

									lowercase_ : Dict							       =   {'''image_embeds''': image_embeds, '''hint''': hint}
									lowercase_ : Dict							       =   self.unet(
									    sample=A    ,						timestep=A    ,						encoder_hidden_states=A    ,						added_cond_kwargs=A    ,						return_dict=A    ,						)[0]

									if do_classifier_free_guidance:
											lowercase_       ,					lowercase_ : Tuple							       =   noise_pred.split(latents.shape[1]    ,						dim=1   )
											lowercase_       ,					lowercase_ : Optional[int]							       =   noise_pred.chunk(2   )
											lowercase_       ,					lowercase_ : List[str]							       =   variance_pred.chunk(2   )
											lowercase_ : Any							       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											lowercase_ : Optional[int]							       =   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"]
									):
											lowercase_       ,					lowercase_ : Optional[Any]							       =   noise_pred.split(latents.shape[1]    ,						dim=1   )

									# compute the previous noisy sample x_t -> x_t-1
									lowercase_ : Union[str, Any]							       =   self.scheduler.step(
									    A    ,						A    ,						A    ,						generator=A    ,						)[0]
							# post-processing
							lowercase_ : List[Any]							       =   self.movq.decode(A    ,						force_not_quantize=A   )['''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"]:
									lowercase_ : List[Any]							       =   image * 0.5 + 0.5
									lowercase_ : Any							       =   image.clamp(0    ,						1   )
									lowercase_ : Optional[Any]							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   ).float().numpy()

							if output_type == "pil":
									lowercase_ : int							       =   self.numpy_to_pil(A   )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=A   )











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



