





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCamelCase :    int  							= {
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Dict  							= {
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Tuple  							= {
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Dict  							= {
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_lowerCamelCase :    Tuple  							= {
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_lowerCamelCase :    Any  							= {
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       ):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("boolean value expected"       )




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[int]						,				SCREAMING_SNAKE_CASE__      :  int						,				SCREAMING_SNAKE_CASE__      :  Optional[int]						,				SCREAMING_SNAKE_CASE__      :  Any						,				SCREAMING_SNAKE_CASE__      :  Optional[int]=False       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							SCREAMING_SNAKE_CASE__       :      str     =						checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							SCREAMING_SNAKE_CASE__       :      int     =						checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.skip_connection.weight''']
														SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint




def _a (  SCREAMING_SNAKE_CASE__      :  Any						,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]						,				SCREAMING_SNAKE_CASE__      :  List[Any]						,				SCREAMING_SNAKE_CASE__      :  Optional[Any]						,				SCREAMING_SNAKE_CASE__      :  List[str]=None       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3						,				dim=0       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3						,				dim=0       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint[f'''{old_prefix}.norm.weight''']
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						checkpoint[f'''{old_prefix}.norm.bias''']

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						weight_q.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      str     =						bias_q.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						weight_k.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      int     =						bias_k.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						weight_v.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      Dict     =						bias_v.squeeze(-1       ).squeeze(-1       )

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1       ).squeeze(-1       )
							)
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1       ).squeeze(-1       )

							return new_checkpoint




def _a (  SCREAMING_SNAKE_CASE__      :  str						,				SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    Any:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.load(SCREAMING_SNAKE_CASE__						,				map_location="cpu"       )
							SCREAMING_SNAKE_CASE__       :      str     =						{}

							SCREAMING_SNAKE_CASE__       :      List[str]     =						checkpoint["time_embed.0.weight"]
							SCREAMING_SNAKE_CASE__       :      str     =						checkpoint["time_embed.0.bias"]
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint["time_embed.2.weight"]
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						checkpoint["time_embed.2.bias"]

							if unet_config["num_class_embeds"] is not None:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint["label_emb.weight"]

							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint["input_blocks.0.0.weight"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint["input_blocks.0.0.bias"]

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						unet_config["down_block_types"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						unet_config["layers_per_block"]
							SCREAMING_SNAKE_CASE__       :      Dict     =						unet_config["attention_head_dim"]
							SCREAMING_SNAKE_CASE__       :      Any     =						unet_config["block_out_channels"]
							SCREAMING_SNAKE_CASE__       :      str     =						1
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						channels_list[0]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Any     =						channels_list[i]
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE__       ):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''down_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      int     =						f'''input_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						True if j == 0 and downsample_block_has_skip else False
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				has_skip=SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE__       ):
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''down_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''input_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      int     =						True if j == 0 and downsample_block_has_skip else False
																												SCREAMING_SNAKE_CASE__       :      Any     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				has_skip=SCREAMING_SNAKE_CASE__       )
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''down_blocks.{i}.attentions.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''input_blocks.{current_layer}.1'''
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						convert_attention(
																												    SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

														if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''down_blocks.{i}.downsamplers.0'''
																					SCREAMING_SNAKE_CASE__       :      int     =						f'''input_blocks.{current_layer}.0'''
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
																					current_layer += 1

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						current_channels

							# hardcoded the mid-block for now
							SCREAMING_SNAKE_CASE__       :      str     =						"mid_block.resnets.0"
							SCREAMING_SNAKE_CASE__       :      int     =						"middle_block.0"
							SCREAMING_SNAKE_CASE__       :      Any     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						"mid_block.attentions.0"
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"middle_block.1"
							SCREAMING_SNAKE_CASE__       :      Any     =						convert_attention(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"mid_block.resnets.1"
							SCREAMING_SNAKE_CASE__       :      str     =						"middle_block.2"
							SCREAMING_SNAKE_CASE__       :      List[str]     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      int     =						0
							SCREAMING_SNAKE_CASE__       :      int     =						unet_config["up_block_types"]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__       ):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1       ):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''up_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						f'''output_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				has_skip=SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						f'''up_blocks.{i}.upsamplers.0'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''output_blocks.{current_layer-1}.1'''
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1       ):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''up_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''output_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				has_skip=SCREAMING_SNAKE_CASE__       )
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''up_blocks.{i}.attentions.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						f'''output_blocks.{current_layer}.1'''
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						convert_attention(
																												    SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																												SCREAMING_SNAKE_CASE__       :      int     =						f'''up_blocks.{i}.upsamplers.0'''
																												SCREAMING_SNAKE_CASE__       :      Dict     =						f'''output_blocks.{current_layer-1}.2'''
																												SCREAMING_SNAKE_CASE__       :      int     =						convert_resnet(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Any     =						checkpoint["out.0.weight"]
							SCREAMING_SNAKE_CASE__       :      Tuple     =						checkpoint["out.0.bias"]
							SCREAMING_SNAKE_CASE__       :      Any     =						checkpoint["out.2.weight"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint["out.2.bias"]

							return new_checkpoint


if __name__ == "__main__":
		_lowerCamelCase :    Optional[Any]  							= argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		_lowerCamelCase :    List[Any]  							= parser.parse_args()
		_lowerCamelCase :    int  							= strabool(args.class_cond)

		_lowerCamelCase :    int  							= os.path.basename(args.unet_path)
		print(f"Checkpoint: {ckpt_name}")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				_lowerCamelCase :    str  							= IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowerCamelCase :    Any  							= LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				_lowerCamelCase :    Any  							= TEST_UNET_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		if not args.class_cond:
				_lowerCamelCase :    Any  							= None

		_lowerCamelCase :    Tuple  							= con_pt_to_diffuser(args.unet_path, unet_config)

		_lowerCamelCase :    str  							= UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				_lowerCamelCase :    Optional[int]  							= CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				_lowerCamelCase :    Optional[int]  							= CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowerCamelCase :    Optional[int]  							= CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		_lowerCamelCase :    Union[str, Any]  							= CMStochasticIterativeScheduler(**scheduler_config)

		_lowerCamelCase :    List[str]  							= ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)













from __future__ import annotations

import time

_lowerCamelCase :    Tuple  							= list[tuple[int, int]]

_lowerCamelCase :    int  							= [
    [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 :    str  							= [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Node | None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_y
														SCREAMING_SNAKE_CASE__       :      str     =						(pos_y, pos_x)
														SCREAMING_SNAKE_CASE__       :      Dict     =						goal_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						goal_y
														SCREAMING_SNAKE_CASE__       :      str     =						parent


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					tuple[int, int],			_UpperCAmelCase     :					tuple[int, int]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						Node(start[1],			start[0],			goal[1],			goal[0],			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						Node(goal[1],			goal[0],			goal[1],			goal[0],			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.start]
														SCREAMING_SNAKE_CASE__       :      str     =						False






							def  A_     ( self     :					Dict						)				->			Path | None:





														"""simple docstring"""
														while self.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.node_queue.pop(0						)

																					if current_node.pos == self.target.pos:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						True
																												return self.retrace_path(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_successors(_UpperCAmelCase						)

																					for node in successors:
																												self.node_queue.append(_UpperCAmelCase						)

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






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Node						)				->			list[Node]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						[]
														for action in delta:
																					SCREAMING_SNAKE_CASE__       :      str     =						parent.pos_x + action[1]
																					SCREAMING_SNAKE_CASE__       :      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,			_UpperCAmelCase						)						)
														return successors






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Node | None						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x)						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_node.parent
														path.reverse()
														return path


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						False






							def  A_     ( self     :					List[str]						)				->			Path | None:





														"""simple docstring"""
														while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.fwd_bfs.node_queue.pop(0						)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.bwd_bfs.node_queue.pop(0						)

																					if current_bwd_node.pos == current_fwd_node.pos:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						True
																												return self.retrace_bidirectional_path(
																												    _UpperCAmelCase,			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_bwd_node
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_fwd_node

																					SCREAMING_SNAKE_CASE__       :      List[str]     =						{
																					    self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase						),
																					    self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase						),
																					}

																					for bfs in [self.fwd_bfs, self.bwd_bfs]:
																												for node in successors[bfs]:
																																			bfs.node_queue.append(_UpperCAmelCase						)

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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Node,			_UpperCAmelCase     :					Node						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.fwd_bfs.retrace_path(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						self.bwd_bfs.retrace_path(_UpperCAmelCase						)
														bwd_path.pop()
														bwd_path.reverse()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						fwd_path + bwd_path
														return path


if __name__ == "__main__":
		# all coordinates are given in format [y,x]
		import doctest

		doctest.testmod()
		_lowerCamelCase :    Optional[Any]  							= (0, 0)
		_lowerCamelCase :    Dict  							= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		_lowerCamelCase :    Dict  							= time.time()
		_lowerCamelCase :    List[Any]  							= BreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bfs.search()
		_lowerCamelCase :    Dict  							= time.time() - start_bfs_time

		print('''Unidirectional BFS computation time : ''', bfs_time)

		_lowerCamelCase :    str  							= time.time()
		_lowerCamelCase :    Dict  							= BidirectionalBreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bd_bfs.search()
		_lowerCamelCase :    Optional[Any]  							= time.time() - start_bd_bfs_time

		print('''Bidirectional BFS computation time : ''', bd_bfs_time)






