





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A    :					List[Any] =      False
A    :					Optional[int] =      True
A    :					Tuple =      False


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

					parser.add_argument(
					    '''--repo_path''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the architecture.''',
					)

					parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

					A    :					Dict =      parser.parse_args()

					A    :					Any =      {
					    '''image_size''': '''sample_size''',
					    '''num_res_blocks''': '''layers_per_block''',
					    '''block_channels''': '''block_out_channels''',
					    '''down_blocks''': '''down_block_types''',
					    '''up_blocks''': '''up_block_types''',
					    '''downscale_freq_shift''': '''freq_shift''',
					    '''resnet_num_groups''': '''norm_num_groups''',
					    '''resnet_act_fn''': '''act_fn''',
					    '''resnet_eps''': '''norm_eps''',
					    '''num_head_channels''': '''attention_head_dim''',
					}

					A    :					Dict =      {
					    '''time_steps''': '''time_proj''',
					    '''mid''': '''mid_block''',
					    '''downsample_blocks''': '''down_blocks''',
					    '''upsample_blocks''': '''up_blocks''',
					}

					A    :					Union[str, Any] =      '''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

					with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
										A    :					int =      reader.read()
										A    :					Any =      json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, '''config.json'''):
										A    :					str =      UNetaDModel(**config)
					else:
										A    :					int =      UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
										A    :					Optional[Any] =      class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					A    :					Optional[Any] =      dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				A    :					Optional[int] =      config[key]
																				del config[key]

										A    :					str =      [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
										A    :					str =      [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

					if do_only_weights:
										A    :					List[str] =      torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

										A    :					Any =      {}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
																				continue
															A    :					str =      False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('''.''')[0] == key:
																									A    :					Optional[int] =      param_value
																									A    :					str =      True
															if not has_changed:
																				A    :					List[str] =      param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))










from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A    :					str =      0

A    :					Any =      [
    [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    :					Dict =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A    :					Union[str, Any] =      tuple[int, int]


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Node | None							,	)	->	None:







					"""simple docstring"""






					A__        =   pos_x
					A__        =   pos_y
					A__        =   (pos_y, pos_x)
					A__        =   goal_x
					A__        =   goal_y
					A__        =   g_cost
					A__        =   parent
					A__        =   self.calculate_heuristic()
					A__        =   self.g_cost + self.h_cost






			def    a_       (			self   :    Dict   )	->	float:







					"""simple docstring"""






					A__        =   self.pos_x - self.goal_x
					A__        =   self.pos_y - self.goal_y
					if HEURISTIC == 1:
							return abs(__lowerCAmelCase   ) + abs(__lowerCAmelCase   )
					else:
							return sqrt(dy**2 + dx**2   )







			def __lt__(			self   :    int							,	__lowerCAmelCase   :    Node   )	->	bool:







					"""simple docstring"""






					return self.f_cost < other.f_cost


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Union[str, Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	Tuple:







					"""simple docstring"""






					A__        =   Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	0							,	__lowerCAmelCase   )
					A__        =   Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	9_99_99							,	__lowerCAmelCase   )

					A__        =   [self.start]
					A__        =   []

					A__        =   False






			def    a_       (			self   :    List[str]   )	->	list[TPosition]:







					"""simple docstring"""






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

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

							self.closed_nodes.append(__lowerCAmelCase   )
							A__        =   self.get_successors(__lowerCAmelCase   )

							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(__lowerCAmelCase   )
									else:
											# retrieve the best current path
											A__        =   self.open_nodes.pop(self.open_nodes.index(__lowerCAmelCase   )   )

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

					return [self.start.pos]






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Node   )	->	list[Node]:







					"""simple docstring"""






					A__        =   []
					for action in delta:
							A__        =   parent.pos_x + action[1]
							A__        =   parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__lowerCAmelCase   ) - 1):
									continue

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

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







			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Node | None   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   node
					A__        =   []
					while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)   )
							A__        =   current_node.parent
					path.reverse()
					return path


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    TPosition							,	__lowerCAmelCase   :    TPosition   )	->	None:







					"""simple docstring"""






					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   AStar(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   False






			def    a_       (			self   :    int   )	->	list[TPosition]:







					"""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__        =   self.fwd_astar.open_nodes.pop(0   )
							A__        =   self.bwd_astar.open_nodes.pop(0   )

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

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

							A__        =   current_bwd_node
							A__        =   current_fwd_node

							A__        =   {
							    self.fwd_astar: self.fwd_astar.get_successors(__lowerCAmelCase   ),
							    self.bwd_astar: self.bwd_astar.get_successors(__lowerCAmelCase   ),
							}

							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(__lowerCAmelCase   )
											else:
													# retrieve the best current path
													A__        =   astar.open_nodes.pop(
													    astar.open_nodes.index(__lowerCAmelCase   )   )

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

					return [self.fwd_astar.start.pos]







			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    Node							,	__lowerCAmelCase   :    Node   )	->	list[TPosition]:







					"""simple docstring"""






					A__        =   self.fwd_astar.retrace_path(__lowerCAmelCase   )
					A__        =   self.bwd_astar.retrace_path(__lowerCAmelCase   )
					bwd_path.pop()
					bwd_path.reverse()
					A__        =   fwd_path + bwd_path
					return path


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

					A    :					Dict =      time.time()
					A    :					Optional[Any] =      AStar(init, goal)
					A    :					Optional[int] =      a_star.search()
					A    :					Optional[int] =      time.time() - start_time
					print(F'''AStar execution time = {end_time:f} seconds''')

					A    :					Dict =      time.time()
					A    :					Tuple =      BidirectionalAStar(init, goal)
					A    :					List[Any] =      time.time() - bd_start_time
					print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')



