





"""simple docstring"""




import numpy as np


class        __UpperCamelCase				:






							def __init__(   self      )	-> int:
								a      :		Tuple	=				(0, 0)
								a      :		Any	=				None
								a      :		int	=				0
								a      :		Optional[int]	=				0
								a      :		List[str]	=				0






							def __eq__(   self      ,					lowerCAmelCase__      )	-> Optional[Any]:
								return self.position == cell.position






							def     __a   (   self      )	-> List[Any]:
								print(self.position      )


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__=(5, 5)      )	-> Any:
								a      :		Union[str, Any]	=				np.zeros(lowerCAmelCase__      )
								a      :		List[str]	=				world_size[0]
								a      :		Union[str, Any]	=				world_size[1]






							def     __a   (   self      )	-> Optional[Any]:
								print(self.w      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								a      :		List[Any]	=				[
								    (-1, -1),
								    (-1, 0),
								    (-1, 1),
								    (0, -1),
								    (0, 1),
								    (1, -1),
								    (1, 0),
								    (1, 1),
								]
								a      :		Optional[Any]	=				cell.position[0]
								a      :		List[str]	=				cell.position[1]
								a      :		int	=				[]
								for n in neughbour_cord:
									a      :		Dict	=				current_x + n[0]
									a      :		List[str]	=				current_y + n[1]
									if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
										a      :		Tuple	=				Cell()
										a      :		int	=				(x, y)
										a      :		int	=				cell
										neighbours.append(lowerCAmelCase__      )
								return neighbours






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a      :		Optional[Any]	=				[]
	a      :		Union[str, Any]	=				[]
	_open.append(_lowercase					)

	while _open:
		a      :		List[str]	=				np.argmin([n.f for n in _open]					)
		a      :		Optional[int]	=				_open[min_f]
		_closed.append(_open.pop(_lowercase					)					)
		if current == goal:
			break
		for n in world.get_neigbours(_lowercase					):
			for c in _closed:
				if c == n:
					continue
			a      :		List[str]	=				current.g + 1
			a,       a      :		Any	=				n.position
			a,       a      :		List[Any]	=				goal.position
			a      :		Optional[Any]	=				(ya - ya) ** 2 + (xa - xa) ** 2
			a      :		str	=				n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_lowercase					)
	a      :		List[Any]	=				[]
	while current.parent is not None:
		path.append(current.position					)
		a      :		Tuple	=				current.parent
	path.append(current.position					)
	return path[::-1]


if __name__ == "__main__":
		a						: int     	=			Gridworld()
		# Start position and goal
		a						: List[str]     	=			Cell()
		a						: str     	=			(0, 0)
		a						: str     	=			Cell()
		a						: Optional[int]     	=			(4, 4)
		print(F'''path from {start.position} to {goal.position}''')
		a						: int     	=			astar(world, start, goal)
		#   Just for visual reasons.
		for i in s:
				a						: Optional[Any]     	=			1
		print(world.w)









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

								if output_type == "pil":
									a      :		int	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


