




# using dfs for finding eulerian path traversal




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None					)  ->       Any:
   snake_case				:    Union[str, Any]           =	(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         snake_case     , snake_case				:    int           =	True, True
         snake_case				:    List[Any]           =	dfs(lowercase						,lowercase						,lowercase						,lowercase					)
   return path




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	0
   snake_case				:    Union[str, Any]           =	-1
   for i in range(lowercase					):
      if i not in graph.keys():
         continue
      if len(graph[i]					) % 2 == 1:
         odd_degree_nodes += 1
         snake_case				:    str           =	i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	[[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
   snake_case     , snake_case				:    Any           =	check_circuit_or_path(lowercase						,lowercase					)
   if check == 3:
      print("""graph is not Eulerian"""					)
      print("""no path"""					)
      return
   snake_case				:    str           =	1
   if check == 2:
      snake_case				:    Optional[int]           =	odd_node
      print("""graph has a Euler path"""					)
   if check == 1:
      print("""graph has a Euler cycle"""					)
   snake_case				:    Dict           =	dfs(lowercase						,lowercase						,lowercase					)
   print(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   snake_case				:    List[Any]           =	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   snake_case				:    Tuple           =	{
       1: [],
       2: []
       # all degree is zero
   }
   snake_case				:    Tuple           =	10
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)


if __name__ == "__main__":
       main()











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

lowerCamelCase	:	Dict				=   '\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__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				)		->    int:
         super().__init__()

         self.register_modules(
             unet=A    ,				scheduler=A    ,				movq=A    ,				)
         snake_case				:    Tuple           =	2 ** (len(self.movq.config.block_out_channels     ) - 1)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[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}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

         snake_case				:    Optional[Any]           =	latents * scheduler.init_noise_sigma
         return latents

      def      UpperCAmelCase				(				self    ,				A=0     )		->    List[str]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

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

      def      UpperCAmelCase				(				self    ,				A=0     )		->    Optional[Any]:
         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."""     )

         snake_case				:    int           =	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)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

         # We'll offload the last model manually.
         snake_case				:    Tuple           =	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    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    ,				A    ,				A    ,				A    ,				A = 5_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

         snake_case				:    List[Any]           =	image_embeds.shape[0] * num_images_per_prompt

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

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

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

         # create initial latent
         snake_case				:    str           =	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
            snake_case				:    str           =	torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

            snake_case				:    Any           =	{"""image_embeds""": image_embeds, """hint""": hint}
            snake_case				:    List[Any]           =	self.unet(
                sample=A    ,				timestep=A    ,				encoder_hidden_states=A    ,				added_cond_kwargs=A    ,				return_dict=A    ,				)[0]

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

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

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





