"""simple docstring"""





import heapq
import sys

import numpy as np

__A  =				tuple[int, int]
class  _snake_case     :






						def __init__(       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Any		   =     []
													__lowerCamelCase					:		List[Any]		   =     set()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													if not self.empty():
																				return self.elements[0][0]
													else:
																				return float("inf"    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return len(self.elements    ) == 0






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any]    ):
													if item not in self.set:
																				heapq.heappush(self.elements							,					(priority, item)    )
																				self.set.add(UpperCAmelCase    )
													else:
																				# update
																				# print("update", item)
																				__lowerCamelCase					:		List[Any]		   =     []
																				((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Any		   =     heapq.heappop(self.elements    )
																				while x != item:
																											temp.append((pri, x)    )
																											((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Tuple		   =     heapq.heappop(self.elements    )
																				temp.append((priority, item)    )
																				for pro, xxx in temp:
																											heapq.heappush(self.elements							,					(pro, xxx)    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple    ):
													if item in self.set:
																				self.set.remove(UpperCAmelCase    )
																				__lowerCamelCase					:		Dict		   =     []
																				((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Any		   =     heapq.heappop(self.elements    )
																				while x != item:
																											temp.append((pro, x)    )
																											((__lowerCamelCase)   ,  (__lowerCamelCase))					:		List[Any]		   =     heapq.heappop(self.elements    )
																				for prito, yyy in temp:
																											heapq.heappush(self.elements							,					(prito, yyy)    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													return self.elements[0][1]







						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Optional[Any]		   =     heapq.heappop(self.elements    )
													self.set.remove(UpperCAmelCase    )
													return (priority, item)


def    lowercase_   (   _lowerCamelCase:	TPos						,   _lowerCamelCase:	TPos      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     np.array(_lowerCamelCase      )
							__lowerCamelCase					:		Optional[int]		   =     np.array(_lowerCamelCase      )
							return np.linalg.norm(a - b      )


def    lowercase_   (   _lowerCamelCase:	TPos						,   _lowerCamelCase:	TPos      ) ->      List[Any]:



							'''simple docstring'''

							return consistent_heuristic(_lowerCamelCase						,   _lowerCamelCase      ) // t


def    lowercase_   (   _lowerCamelCase:	TPos						,   _lowerCamelCase:	TPos      ) ->      Dict:



							'''simple docstring'''

							return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def    lowercase_   (   _lowerCamelCase:	TPos						,   _lowerCamelCase:	int						,   _lowerCamelCase:	TPos						,   _lowerCamelCase:	dict[TPos, float]      ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     g_function[start] + Wa * heuristics[i](_lowerCamelCase						,   _lowerCamelCase      )
							return ans


def    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[str]      ) ->      Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     np.chararray((n, n)      )
							for i in range(_lowerCamelCase      ):
														for j in range(_lowerCamelCase      ):
																					__lowerCamelCase					:		Union[str, Any]		   =     "*"

							for i in range(_lowerCamelCase      ):
														for j in range(_lowerCamelCase      ):
																					if (j, (n - 1) - i) in blocks:
																												__lowerCamelCase					:		Any		   =     "#"

							__lowerCamelCase					:		Any		   =     "-"
							__lowerCamelCase					:		Optional[Any]		   =     back_pointer[goal]
							while x != start:
														((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Any		   =     x
														# print(x)
														__lowerCamelCase					:		List[Any]		   =     "-"
														__lowerCamelCase					:		List[str]		   =     back_pointer[x]
							__lowerCamelCase					:		Optional[Any]		   =     "-"

							for i in range(_lowerCamelCase      ):
														for j in range(_lowerCamelCase      ):
																					if (i, j) == (0, n - 1):
																												print(grid[i][j]						,   end=" "      )
																												print("<-- End position"						,   end=" "      )
																					else:
																												print(grid[i][j]						,   end=" "      )
														print()
							print("^"      )
							print("Start position"      )
							print()
							print("# is an obstacle"      )
							print("- is the path taken by algorithm"      )
							print("PATH TAKEN BY THE ALGORITHM IS:-"      )
							__lowerCamelCase					:		Union[str, Any]		   =     back_pointer[goal]
							while x != start:
														print(_lowerCamelCase						,   end=" "      )
														__lowerCamelCase					:		Dict		   =     back_pointer[x]
							print(_lowerCamelCase      )
							sys.exit()


def    lowercase_   (   _lowerCamelCase:	TPos      ) ->      List[Any]:



							'''simple docstring'''

							if p[0] < 0 or p[0] > n - 1:
														return False
							if p[1] < 0 or p[1] > n - 1:
														return False
							return True


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	int						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	List[Any]						,   ) ->      int:



							'''simple docstring'''

							for itera in range(_lowerCamelCase      ):
														open_list[itera].remove_element(_lowerCamelCase      )
							# print("s", s)
							# print("j", j)
							((__lowerCamelCase)   ,  (__lowerCamelCase))					:		Tuple		   =     s
							__lowerCamelCase					:		Optional[int]		   =     (x - 1, y)
							__lowerCamelCase					:		str		   =     (x + 1, y)
							__lowerCamelCase					:		Tuple		   =     (x, y + 1)
							__lowerCamelCase					:		Any		   =     (x, y - 1)

							for neighbours in [left, right, up, down]:
														if neighbours not in blocks:
																					if valid(_lowerCamelCase      ) and neighbours not in visited:
																												# print("neighbour", neighbours)
																												visited.add(_lowerCamelCase      )
																												__lowerCamelCase					:		Optional[int]		   =     -1
																												__lowerCamelCase					:		Optional[Any]		   =     float("inf"      )

																					if valid(_lowerCamelCase      ) and g_function[neighbours] > g_function[s] + 1:
																												__lowerCamelCase					:		Tuple		   =     g_function[s] + 1
																												__lowerCamelCase					:		Union[str, Any]		   =     s
																												if neighbours not in close_list_anchor:
																																			open_list[0].put(_lowerCamelCase						,   key(_lowerCamelCase						,   0						,   _lowerCamelCase						,   _lowerCamelCase      )      )
																																			if neighbours not in close_list_inad:
																																										for var in range(1						,   _lowerCamelCase      ):
																																																	if key(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      ) <= Wa * key(
																																																	    _lowerCamelCase						,   0						,   _lowerCamelCase						,   _lowerCamelCase      ):
																																																								open_list[j].put(
																																																								    _lowerCamelCase						,   key(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )      )


def    lowercase_   (   ) ->      Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     []
							for x in range(1						,   5      ):
														for y in range(1						,   6      ):
																					some_list.append((x, y)      )

							for x in range(15						,   20      ):
														some_list.append((x, 17)      )

							for x in range(10						,   19      ):
														for y in range(1						,   15      ):
																					some_list.append((x, y)      )

    # L block
							for x in range(1						,   4      ):
														for y in range(12						,   19      ):
																					some_list.append((x, y)      )
							for x in range(3						,   13      ):
														for y in range(16						,   19      ):
																					some_list.append((x, y)      )
							return some_list


__A  =				{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__A  =				[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
__A  =				make_common_ground()


__A  =				blocks_blk
# hyper parameters
__A  =				1
__A  =				1
__A  =				20
__A  =				3  # one consistent and two other inconsistent

# start and end destination
__A  =				(0, 0)
__A  =				(n - 1, n - 1)

__A  =				1


def    lowercase_   (   _lowerCamelCase:	TPos						,   _lowerCamelCase:	TPos						,   _lowerCamelCase:	int      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     {start: 0, goal: float("inf"      )}
							__lowerCamelCase					:		Any		   =     {start: -1, goal: -1}
							__lowerCamelCase					:		Tuple		   =     []
							__lowerCamelCase					:		Any		   =     set()

							for i in range(_lowerCamelCase      ):
														open_list.append(PriorityQueue()      )
														open_list[i].put(_lowerCamelCase						,   key(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )      )

							__lowerCamelCase					:		list[int]		   =     []
							__lowerCamelCase					:		list[int]		   =     []
							while open_list[0].minkey() < float("inf"      ):
														for i in range(1						,   _lowerCamelCase      ):
																					# print(open_list[0].minkey(), open_list[i].minkey())
																					if open_list[i].minkey() <= Wa * open_list[0].minkey():
																												global t
																												t += 1
																												if g_function[goal] <= open_list[i].minkey():
																																			if g_function[goal] < float("inf"      ):
																																										do_something(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
																												else:
																																			__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     open_list[i].top_show()
																																			visited.add(_lowerCamelCase      )
																																			expand_state(
																																			    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   )
																																			close_list_inad.append(_lowerCamelCase      )
																					else:
																												if g_function[goal] <= open_list[0].minkey():
																																			if g_function[goal] < float("inf"      ):
																																										do_something(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
																												else:
																																			__lowerCamelCase					:		int		   =     open_list[0].top_show()
																																			visited.add(_lowerCamelCase      )
																																			expand_state(
																																			    _lowerCamelCase						,   0						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   )
																																			close_list_anchor.append(_lowerCamelCase      )
							print("No path found to goal"      )
							print()
							for i in range(n - 1						,   -1						,   -1      ):
														for j in range(_lowerCamelCase      ):
																					if (j, i) in blocks:
																												print("#"						,   end=" "      )
																					elif (j, i) in back_pointer:
																												if (j, i) == (n - 1, n - 1):
																																			print("*"						,   end=" "      )
																												else:
																																			print("-"						,   end=" "      )
																					else:
																												print("*"						,   end=" "      )
																					if (j, i) == (n - 1, n - 1):
																												print("<-- End position"						,   end=" "      )
														print()
							print("^"      )
							print("Start position"      )
							print()
							print("# is an obstacle"      )
							print("- is the path taken by algorithm"      )


if __name__ == "__main__":
				multi_a_star(start, goal, n_heuristic)
"""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  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													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      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

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

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

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

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# 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+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

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

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		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
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )