'''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,
)


SCREAMING_SNAKE_CASE_:	List[str]    =logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_:	List[str]    ='\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       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Optional[int]   ,       snake_case_			:					Union[str, Any]=8     )       -> str:


     '''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






class 			__A (					UpperCamelCase__      ):



    def __init__(self       :       List[str]  ,     __a       :       UNetaDConditionModel  ,     __a       :       DDPMScheduler  ,     __a       :       VQModel  ,     ):
         super().__init__()

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



    def    _lowercase		(self       :       List[str]  ,     __a       :       Optional[int]  ,     __a       :       Optional[int]  ,     __a       :       List[str]  ,     __a       :       Any  ,     __a       :       Optional[int]  ,     __a       :       int					):
         if latents is None:
              UpperCAmelCase_			       =  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}"""					)
              UpperCAmelCase_			       =  latents.to(a_					)

         UpperCAmelCase_			       =  latents * scheduler.init_noise_sigma
         return latents



    def    _lowercase		(self       :       Any  ,     __a       :       Union[str, Any]=0					):
         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(a_  ,     a_					)



    def    _lowercase		(self       :       Any  ,     __a       :       Optional[Any]=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."					)

         UpperCAmelCase_			       =  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)

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

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



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    _lowercase		(self       :       List[Any]					):
         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[str]  ,     __a       :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     __a       :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     __a       :       torch.FloatTensor  ,     __a       :       int = 512  ,     __a       :       int = 512  ,     __a       :       int = 100  ,     __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  ,     ):
         UpperCAmelCase_			       =  self._execution_device

         UpperCAmelCase_			       =  guidance_scale > 1.0

         if isinstance(a_  ,     a_					):
              UpperCAmelCase_			       =  torch.cat(a_  ,     dim=0					)
         if isinstance(a_  ,     a_					):
              UpperCAmelCase_			       =  torch.cat(a_  ,     dim=0					)
         if isinstance(a_  ,     a_					):
              UpperCAmelCase_			       =  torch.cat(a_  ,     dim=0					)

         UpperCAmelCase_			       =  image_embeds.shape[0] * num_images_per_prompt

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

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

         UpperCAmelCase_			       =  self.movq.config.latent_channels

         UpperCAmelCase_			       =  downscale_height_and_width(a_  ,     a_  ,     self.movq_scale_factor					)

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

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

              if do_classifier_free_guidance:
                   UpperCAmelCase_			       =  noise_pred.split(latents.shape[1]  ,     dim=1					)
                   UpperCAmelCase_			       =  noise_pred.chunk(2					)
                   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_			       =  noise_pred.split(latents.shape[1]  ,     dim=1					)

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_			       =  self.scheduler.step(
                  a_  ,     a_  ,     a_  ,     generator=a_  ,     )[0]
         # post-processing
         UpperCAmelCase_			       =  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"]:
              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(a_					)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a_					)









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=self.get_successors(a_			)

													for child_node in successors:
																if child_node in self.closed_nodes:
																			continue

																if child_node not in self.open_nodes:
																			self.open_nodes.append(a_			)
																else:
																			# retrieve the best current path
																			snake_case:    List[Any]      							=self.open_nodes.pop(self.open_nodes.index(a_			)			)

																			if child_node.g_cost < better_node.g_cost:
																						self.open_nodes.append(a_			)
																			else:
																						self.open_nodes.append(a_			)

										if not self.reached:
													return [self.start.pos]
										return None


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=parent.pos_y + action[0]

													if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(a_			) - 1):
																continue

													if grid[pos_y][pos_x] != 0:
																continue

													successors.append(
													    Node(
													        a_      ,						a_      ,						self.target.pos_y      ,						self.target.pos_x      ,						parent.g_cost + 1      ,						a_      ,						)			)
										return successors




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	a							   =				(0, 0)
	a							   =				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




