

'''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,
)


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

__lowerCamelCase						    =      '''
    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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__=8			)     ->     int:
       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







class    A__ ( _snake_case     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			)  -> Optional[Any]:





           '''simple docstring'''
           super().__init__()

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           if latents is None:
                  A_		   = randn_tensor(UpperCamelCase__						,			generator=UpperCamelCase__						,			device=UpperCamelCase__						,			dtype=UpperCamelCase__    )
           else:
                  if latents.shape != shape:
                         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
                  A_		   = latents.to(UpperCamelCase__    )

           A_		   = latents * scheduler.init_noise_sigma
           return latents



    def    snake_case_     (   self						,			UpperCamelCase__=0    )  -> Dict:





           '''simple docstring'''
           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(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=0    )  -> List[Any]:





           '''simple docstring'''
           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=UpperCamelCase__    )
                  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(UpperCamelCase__						,			UpperCamelCase__						,			prev_module_hook=UpperCamelCase__    )

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



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           if not hasattr(self.unet						,			"""_hf_hook"""    ):
                  return self.device
           for module in self.unet.modules():
                  if (
                      hasattr(UpperCamelCase__						,			"""_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(UpperCamelCase__    )
    def __call__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 512						,			UpperCamelCase__ = 512						,			UpperCamelCase__ = 100						,			UpperCamelCase__ = 4.0						,			UpperCamelCase__ = 1						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = "pil"						,			UpperCamelCase__ = True						,			)  -> Dict:





           '''simple docstring'''
           A_		   = self._execution_device

           A_		   = guidance_scale > 1.0

           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = torch.cat(UpperCamelCase__						,			dim=0    )
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = torch.cat(UpperCamelCase__						,			dim=0    )
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = torch.cat(UpperCamelCase__						,			dim=0    )

           A_		   = image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                  A_		   = image_embeds.repeat_interleave(UpperCamelCase__						,			dim=0    )
                  A_		   = negative_image_embeds.repeat_interleave(UpperCamelCase__						,			dim=0    )
                  A_		   = hint.repeat_interleave(UpperCamelCase__						,			dim=0    )

           A_		   = torch.cat([negative_image_embeds, image_embeds]						,			dim=0    ).to(dtype=self.unet.dtype						,			device=UpperCamelCase__    )
           A_		   = torch.cat([hint, hint]						,			dim=0    ).to(dtype=self.unet.dtype						,			device=UpperCamelCase__    )
           self.scheduler.set_timesteps(UpperCamelCase__						,			device=UpperCamelCase__    )
           A_		   = self.scheduler.timesteps

           A_		   = self.movq.config.latent_channels

           A_   ,     A_		   = downscale_height_and_width(UpperCamelCase__						,			UpperCamelCase__						,			self.movq_scale_factor    )

           # create initial latent
           A_		   = self.prepare_latents(
               (batch_size, num_channels_latents, height, width)						,			image_embeds.dtype						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			self.scheduler						,			)

           for i, t in enumerate(self.progress_bar(UpperCamelCase__    )    ):
                  # 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=UpperCamelCase__						,			timestep=UpperCamelCase__						,			encoder_hidden_states=UpperCamelCase__						,			added_cond_kwargs=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[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(
                      UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			generator=UpperCamelCase__						,			)[0]
           # post-processing
           A_		   = self.movq.decode(UpperCamelCase__						,			force_not_quantize=UpperCamelCase__    )["""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(UpperCamelCase__    )

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCamelCase__    )



'''simple docstring'''







import heapq as hq
import math
from collections.abc import Iterator


class    A__ :



    def __init__(   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           A_		   = str(id_    )
           A_		   = None
           A_		   = None
           A_		   = []
           A_		   = {}  # {vertex:distance}



    def __lt__(   self						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           return self.key < other.key



    def __repr__(   self    )  -> Dict:





           '''simple docstring'''
           return self.id



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           self.neighbors.append(UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = weight

def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       # add the neighbors:
       graph[a - 1].add_neighbor(graph[b - 1]			)
       graph[b - 1].add_neighbor(graph[a - 1]			)
       # add the edges:
       graph[a - 1].add_edge(graph[b - 1],    UpperCAmelCase__			)
       graph[b - 1].add_edge(graph[a - 1],    UpperCAmelCase__			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     list:
       A_		   = []
       for u in graph:
              A_		   = math.inf
              A_		   = None
       A_		   = 0
       A_		   = graph[:]
       while q:
              A_		   = min(UpperCAmelCase__			)
              q.remove(UpperCAmelCase__			)
              for v in u.neighbors:
                     if (v in q) and (u.edges[v.id] < v.key):
                            A_		   = u
                            A_		   = u.edges[v.id]
       for i in range(1,    len(UpperCAmelCase__			)			):
              a.append((int(graph[i].id			) + 1, int(graph[i].pi.id			) + 1)			)
       return a
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Iterator[tuple]:
       for u in graph:
              A_		   = math.inf
              A_		   = None
       A_		   = 0

       A_		   = list(UpperCAmelCase__			)
       hq.heapify(UpperCAmelCase__			)

       while h:
              A_		   = hq.heappop(UpperCAmelCase__			)
              for v in u.neighbors:
                     if (v in h) and (u.edges[v.id] < v.key):
                            A_		   = u
                            A_		   = u.edges[v.id]
                            hq.heapify(UpperCAmelCase__			)

       for i in range(1,    len(UpperCAmelCase__			)			):
              yield (int(graph[i].id			) + 1, int(graph[i].pi.id			) + 1)
def  UpperCAmelCase__      ( )     ->     None:
       pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
