





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,
)


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase  :   List[str]						      ="""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=8  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__=5_1_2  ,    lowercase__=5_1_2  ):






   '''simple docstring'''






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





class      A       (	__lowercase						):



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						UNetaDConditionModel		,  _lowerCAmelCase:						DDPMScheduler		,  _lowerCAmelCase:						VQModel		,  )						->    Optional[Any]:
         '''simple docstring'''

         super().__init__()

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Any       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=min(int(num_inference_steps * strength       )		,  _lowerCAmelCase       )

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

         return timesteps, num_inference_steps - t_start



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						int=None       )						->    Tuple:
         '''simple docstring'''

         if not isinstance(_lowerCAmelCase		,  (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(_lowerCAmelCase       )}'       )

         UpperCAmelCase_											=image.to(device=_lowerCAmelCase		,  dtype=_lowerCAmelCase       )

         UpperCAmelCase_											=batch_size * num_images_per_prompt

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

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

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

            UpperCAmelCase_											=self.movq.config.scaling_factor * init_latents

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

         UpperCAmelCase_											=init_latents.shape
         UpperCAmelCase_											=randn_tensor(_lowerCAmelCase		,  generator=_lowerCAmelCase		,  device=_lowerCAmelCase		,  dtype=_lowerCAmelCase       )

         # get latents
         UpperCAmelCase_											=self.scheduler.add_noise(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         UpperCAmelCase_											=init_latents

         return latents



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						List[Any]=0       )						->    List[Any]:
         '''simple docstring'''

         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("Please install accelerate via `pip install accelerate`"       )

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

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



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[str]=0       )						->    Dict:
         '''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."       )

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

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

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

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



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[int]:
         '''simple docstring'''

         if not hasattr(self.unet		,  "_hf_hook"       ):
            return self.device
         for module in self.unet.modules():
            if (
                hasattr(_lowerCAmelCase		,  "_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(_lowerCAmelCase       )
      def __call__(	self:						Dict		,  _lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]		,  _lowerCAmelCase:						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,  _lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 100		,  _lowerCAmelCase:						float = 4.0		,  _lowerCAmelCase:						float = 0.3		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,  _lowerCAmelCase:						Optional[str] = "pil"		,  _lowerCAmelCase:						bool = True		,  )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self._execution_device

         UpperCAmelCase_											=guidance_scale > 1.0

         if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=torch.cat(_lowerCAmelCase		,  dim=0       )
         UpperCAmelCase_											=image_embeds.shape[0]
         if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=torch.cat(_lowerCAmelCase		,  dim=0       )

         if do_classifier_free_guidance:
            UpperCAmelCase_											=image_embeds.repeat_interleave(_lowerCAmelCase		,  dim=0       )
            UpperCAmelCase_											=negative_image_embeds.repeat_interleave(_lowerCAmelCase		,  dim=0       )

         UpperCAmelCase_											=torch.cat([negative_image_embeds, image_embeds]		,  dim=0       ).to(dtype=self.unet.dtype		,  device=_lowerCAmelCase       )

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

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

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

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

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

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_											=self.scheduler.step(
                _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  generator=_lowerCAmelCase		,  )[0]

         # post-processing
         UpperCAmelCase_											=self.movq.decode(_lowerCAmelCase		,  force_not_quantize=_lowerCAmelCase       )["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"]:
            UpperCAmelCase_											=image * 0.5 + 0.5
            UpperCAmelCase_											=image.clamp(0		,  1       )
            UpperCAmelCase_											=image.cpu().permute(0		,  2		,  3		,  1       ).float().numpy()

         if output_type == "pil":
            UpperCAmelCase_											=self.numpy_to_pil(_lowerCAmelCase       )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase       )











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowercase  :   Optional[int]						      =TypeVar("""T""")




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (position - 1) // 2




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 1




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 2

class      A       (	Generic[T]						):



      def __init__(	self:						List[str]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __len__(	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return self.elements



      def __repr__(	self:						Dict       )						->    str:
         '''simple docstring'''

         return str(self.heap       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    bool:
         '''simple docstring'''

         return self.elements == 0



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.heap.append((elem, weight)       )
         UpperCAmelCase_											=self.elements
         self.elements += 1
         self._bubble_up(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    T:
         '''simple docstring'''

         if self.elements > 1:
            self._swap_nodes(0		,  self.elements - 1       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[0]
            self._bubble_down(_lowerCAmelCase       )
         return elem



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_											=(elem, weight)
         if position > 0:
            UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
            if parent_weight > weight:
               self._bubble_up(_lowerCAmelCase       )
            else:
               self._bubble_down(_lowerCAmelCase       )
         else:
            self._bubble_down(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         if curr_pos == 0:
            return None
         UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
         if parent_weight > weight:
            self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
            return self._bubble_up(_lowerCAmelCase       )
         return None



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_											=get_child_left_position(_lowerCAmelCase       )
         UpperCAmelCase_											=get_child_right_position(_lowerCAmelCase       )
         if child_left_position < self.elements and child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < child_left_weight and child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         if child_left_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            if child_left_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         else:
            return None
         if child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         return None






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_						,       UpperCAmelCase_											=(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         UpperCAmelCase_											=nodea_pos
         UpperCAmelCase_											=nodea_pos

class      A       (	Generic[T]						):



      def __init__(	self:						Tuple       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __repr__(	self:						List[str]       )						->    str:
         '''simple docstring'''

         return str(self.connections       )



      def __len__(	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self.nodes



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         if node not in self.connections:
            UpperCAmelCase_											={}
            self.nodes += 1






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.add_node(_lowerCAmelCase       )
         self.add_node(_lowerCAmelCase       )
         UpperCAmelCase_											=weight
         UpperCAmelCase_											=weight




def     a__ (					lowercase__  ,    ):






   '''simple docstring'''






   UpperCAmelCase_											={node: maxsize for node in graph.connections}
   UpperCAmelCase_											={node: None for node in graph.connections}

   UpperCAmelCase_											=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(lowercase__  ,    lowercase__  )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCAmelCase_											=priority_queue.extract_min()
   UpperCAmelCase_											=0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
         UpperCAmelCase_											=node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCAmelCase_											=priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
            UpperCAmelCase_											=node
   return dist, parent




