
'''simple docstring'''






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A			      =      0

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

A			      =      tuple[int, int]




class     __snake_case		:
					def __init__(     self,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= pos_x
									lowerCamelCase					:			Optional[int]									= pos_y
									lowerCamelCase					:			Any									= (pos_y, pos_x)
									lowerCamelCase					:			Optional[Any]									= goal_x
									lowerCamelCase					:			str									= goal_y
									lowerCamelCase					:			Optional[int]									= g_cost
									lowerCamelCase					:			str									= parent
									lowerCamelCase					:			Dict									= self.calculate_heuristic()
									lowerCamelCase					:			str									= self.g_cost + self.h_cost
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.pos_x - self.goal_x
									lowerCamelCase					:			Tuple									= self.pos_y - self.goal_y
									if HEURISTIC == 1:
													return abs(A      ) + abs(A      )
									else:
													return sqrt(dy**2 + dx**2      )




					def __lt__(     self,    A      ):

									"""simple docstring"""


									return self.f_cost < other.f_cost





class     __snake_case		:
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= Node(start[1],    start[0],    goal[1],    goal[0],    0,    A      )
									lowerCamelCase					:			Optional[Any]									= Node(goal[1],    goal[0],    goal[1],    goal[0],    9_9999,    A      )

									lowerCamelCase					:			int									= [self.start]
									lowerCamelCase					:			list[Node]									= []

									lowerCamelCase					:			int									= False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													lowerCamelCase					:			Optional[int]									= self.open_nodes.pop(0      )

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

													self.closed_nodes.append(A      )
													lowerCamelCase					:			Dict									= self.get_successors(A      )

													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(A      )
																	else:
																					# retrieve the best current path
																					lowerCamelCase					:			List[Any]									= self.open_nodes.pop(self.open_nodes.index(A      )      )

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

									return [self.start.pos]
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= []
									for action in delta:
													lowerCamelCase					:			Dict									= parent.pos_x + action[1]
													lowerCamelCase					:			List[Any]									= parent.pos_y + action[0]
													if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(A      ) - 1):
																	continue

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

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




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= node
									lowerCamelCase					:			List[str]									= []
									while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)      )
													lowerCamelCase					:			Union[str, Any]									= current_node.parent
									path.reverse()
									return path





class     __snake_case		:
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AStar(A,    A      )
									lowerCamelCase					:			Optional[Any]									= AStar(A,    A      )
									lowerCamelCase					:			List[str]									= False
					def     UpperCAmelCase_							(     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()
													lowerCamelCase					:			Union[str, Any]									= self.fwd_astar.open_nodes.pop(0      )
													lowerCamelCase					:			Optional[Any]									= self.bwd_astar.open_nodes.pop(0      )

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

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

													lowerCamelCase					:			int									= current_bwd_node
													lowerCamelCase					:			Any									= current_fwd_node

													lowerCamelCase					:			Tuple									= {
													    self.fwd_astar: self.fwd_astar.get_successors(A      ),
													    self.bwd_astar: self.bwd_astar.get_successors(A      ),
													}

													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(A      )
																					else:
																									# retrieve the best current path
																									lowerCamelCase					:			Any									= astar.open_nodes.pop(
																									    astar.open_nodes.index(A      )      )

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

									return [self.fwd_astar.start.pos]




					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.fwd_astar.retrace_path(A      )
									lowerCamelCase					:			List[Any]									= self.bwd_astar.retrace_path(A      )
									bwd_path.pop()
									bwd_path.reverse()
									lowerCamelCase					:			Union[str, Any]									= fwd_path + bwd_path
									return path


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

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

					A			      =      time.time()
					A			      =      BidirectionalAStar(init, goal)
					A			      =      time.time() - bd_start_time
					print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					A			      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
