



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowerCamelCase		       =      0

lowerCamelCase		       =      [
    [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],
]

lowerCamelCase		       =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowerCamelCase		       =      tuple[int, int]


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Tuple      						= pos_x
											a__	:							str      						= pos_y
											a__	:							Any      						= (pos_y, pos_x)
											a__	:							List[str]      						= goal_x
											a__	:							Optional[Any]      						= goal_y
											a__	:							int      						= g_cost
											a__	:							Any      						= parent
											a__	:							List[Any]      						= self.calculate_heuristic()
											a__	:							str      						= self.g_cost + self.h_cost



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.pos_x - self.goal_x
											a__	:							str      						= self.pos_y - self.goal_y
											if HEURISTIC == 1:
															return abs(__UpperCAmelCase				) + abs(__UpperCAmelCase				)
											else:
															return sqrt(dy**2 + dx**2				)




							def __lt__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return self.f_cost < other.f_cost





class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= Node(start[1] ,					start[0] ,					goal[1] ,					goal[0] ,					0 ,					__UpperCAmelCase				)
											a__	:							int      						= Node(goal[1] ,					goal[0] ,					goal[1] ,					goal[0] ,					9_9999 ,					__UpperCAmelCase				)

											a__	:							Any      						= [self.start]
											a__	:							list[Node]      						= []

											a__	:							int      						= False



							def 		_A	(							self				):







											"""simple docstring"""



											while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															a__	:							Dict      						= self.open_nodes.pop(0				)

															if current_node.pos == self.target.pos:
																			return self.retrace_path(__UpperCAmelCase				)

															self.closed_nodes.append(__UpperCAmelCase				)
															a__	:							int      						= self.get_successors(__UpperCAmelCase				)

															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(__UpperCAmelCase				)
																			else:
																							# retrieve the best current path
																							a__	:							Union[str, Any]      						= self.open_nodes.pop(self.open_nodes.index(__UpperCAmelCase				)				)

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

											return [self.start.pos]



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= []
											for action in delta:
															a__	:							Optional[Any]      						= parent.pos_x + action[1]
															a__	:							Union[str, Any]      						= parent.pos_y + action[0]
															if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(__UpperCAmelCase				) - 1):
																			continue

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

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




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= node
											a__	:							List[Any]      						= []
											while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)				)
															a__	:							Dict      						= current_node.parent
											path.reverse()
											return path





class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= AStar(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							Optional[int]      						= AStar(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							str      						= False



							def 		_A	(							self				):







											"""simple docstring"""



											while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
															self.fwd_astar.open_nodes.sort()
															self.bwd_astar.open_nodes.sort()
															a__	:							Dict      						= self.fwd_astar.open_nodes.pop(0				)
															a__	:							Any      						= self.bwd_astar.open_nodes.pop(0				)

															if current_bwd_node.pos == current_fwd_node.pos:
																			return self.retrace_bidirectional_path(
																			    __UpperCAmelCase ,					__UpperCAmelCase				)

															self.fwd_astar.closed_nodes.append(__UpperCAmelCase				)
															self.bwd_astar.closed_nodes.append(__UpperCAmelCase				)

															a__	:							str      						= current_bwd_node
															a__	:							int      						= current_fwd_node

															a__	:							str      						= {
															    self.fwd_astar: self.fwd_astar.get_successors(__UpperCAmelCase				),
															    self.bwd_astar: self.bwd_astar.get_successors(__UpperCAmelCase				),
															}

															for astar in [self.fwd_astar, self.bwd_astar]:
																			for child_node in successors[astar]:
																							if child_node in astar.closed_nodes:
																											continue

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

																											if child_node.g_cost < better_node.g_cost:
																															astar.open_nodes.append(__UpperCAmelCase				)
																											else:
																															astar.open_nodes.append(__UpperCAmelCase				)

											return [self.fwd_astar.start.pos]




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.fwd_astar.retrace_path(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= self.bwd_astar.retrace_path(__UpperCAmelCase				)
											bwd_path.pop()
											bwd_path.reverse()
											a__	:							Union[str, Any]      						= fwd_path + bwd_path
											return path


if __name__ == "__main__":
						# all coordinates are given in format [y,x]
						lowerCamelCase		       =      (0, 0)
						lowerCamelCase		       =      (len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						lowerCamelCase		       =      time.time()
						lowerCamelCase		       =      AStar(init, goal)
						lowerCamelCase		       =      a_star.search()
						lowerCamelCase		       =      time.time() - start_time
						print(F'AStar execution time = {end_time:f} seconds')

						lowerCamelCase		       =      time.time()
						lowerCamelCase		       =      BidirectionalAStar(init, goal)
						lowerCamelCase		       =      time.time() - bd_start_time
						print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

										# update weight
										if len(state_dict[pair_keys[0]].shape   ) == 4:
															SCREAMING_SNAKE_CASE							:     Any						=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     str						=				state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				state_dict[pair_keys[1]].to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# update visited list
										for item in pair_keys:
															visited.append(__lowerCAmelCase   )

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)