






"""simple docstring"""




from __future__ import annotations

import time

__lowerCamelCase :      str										=				list[tuple[int, int]]

__lowerCamelCase :      List[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],
]

__lowerCamelCase :      Optional[int]										=				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Optional[Any]							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  Node | None	):
						snake_case__			:				Dict       =  pos_x
						snake_case__			:				Any       =  pos_y
						snake_case__			:				Any       =  (pos_y, pos_x)
						snake_case__			:				Any       =  goal_x
						snake_case__			:				Any       =  goal_y
						snake_case__			:				Tuple       =  parent



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  tuple[int, int]							,		__A					:  tuple[int, int]	):
						snake_case__			:				List[Any]       =  Node(start[1]							,		start[0]							,		goal[1]							,		goal[0]							,		_lowerCAmelCase	)
						snake_case__			:				Any       =  Node(goal[1]							,		goal[0]							,		goal[1]							,		goal[0]							,		_lowerCAmelCase	)

						snake_case__			:				Optional[Any]       =  [self.start]
						snake_case__			:				List[Any]       =  False



					def 	_lowercase					( self					:  Optional[Any]	):
						while self.node_queue:
							snake_case__			:				Optional[Any]       =  self.node_queue.pop(0	)

							if current_node.pos == self.target.pos:
								snake_case__			:				List[Any]       =  True
								return self.retrace_path(_lowerCAmelCase	)

							snake_case__			:				List[str]       =  self.get_successors(_lowerCAmelCase	)

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

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



					def 	_lowercase					( self					:  List[str]							,		__A					:  Node	):
						snake_case__			:				Optional[int]       =  []
						for action in delta:
							snake_case__			:				Optional[int]       =  parent.pos_x + action[1]
							snake_case__			:				Union[str, Any]       =  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							,		_lowerCAmelCase	)	)
						return successors






					def 	_lowercase					( self					:  Tuple							,		__A					:  Node | None	):
						snake_case__			:				Union[str, Any]       =  node
						snake_case__			:				Any       =  []
						while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)	)
							snake_case__			:				int       =  current_node.parent
						path.reverse()
						return path



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  List[str]							,		__A					:  List[Any]	):
						snake_case__			:				str       =  BreadthFirstSearch(_lowerCAmelCase							,		_lowerCAmelCase	)
						snake_case__			:				Any       =  BreadthFirstSearch(_lowerCAmelCase							,		_lowerCAmelCase	)
						snake_case__			:				int       =  False



					def 	_lowercase					( self					:  Dict	):
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							snake_case__			:				Tuple       =  self.fwd_bfs.node_queue.pop(0	)
							snake_case__			:				Optional[Any]       =  self.bwd_bfs.node_queue.pop(0	)

							if current_bwd_node.pos == current_fwd_node.pos:
								snake_case__			:				Any       =  True
								return self.retrace_bidirectional_path(
								    _lowerCAmelCase							,		_lowerCAmelCase	)

							snake_case__			:				Optional[Any]       =  current_bwd_node
							snake_case__			:				int       =  current_fwd_node

							snake_case__			:				Optional[Any]       =  {
							    self.fwd_bfs: self.fwd_bfs.get_successors(_lowerCAmelCase	),
							    self.bwd_bfs: self.bwd_bfs.get_successors(_lowerCAmelCase	),
							}

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

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






					def 	_lowercase					( self					:  int							,		__A					:  Node							,		__A					:  Node	):
						snake_case__			:				List[str]       =  self.fwd_bfs.retrace_path(_lowerCAmelCase	)
						snake_case__			:				Tuple       =  self.bwd_bfs.retrace_path(_lowerCAmelCase	)
						bwd_path.pop()
						bwd_path.reverse()
						snake_case__			:				Optional[int]       =  fwd_path + bwd_path
						return path


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

	doctest.testmod()
	__lowerCamelCase :      Union[str, Any]										=				(0, 0)
	__lowerCamelCase :      Optional[int]										=				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	__lowerCamelCase :      Tuple										=				time.time()
	__lowerCamelCase :      Any										=				BreadthFirstSearch(init, goal)
	__lowerCamelCase :      List[Any]										=				bfs.search()
	__lowerCamelCase :      Optional[int]										=				time.time() - start_bfs_time

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

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

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











from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  SMALL_MODEL_IDENTIFIER
						snake_case__			:				Any       =  "pt"
						snake_case__			:				Any       =  "tf"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				int       =  AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__A	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  TFAutoModel.from_pretrained(self.test_model							,		from_pt=__A	)
						model_tf.save_pretrained(__A	)



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  "mock_framework"

						# Framework provided - return whatever the user provides
						snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model							,		__A	)
						self.assertEqual(__A							,		__A	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				Optional[int]       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				int       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)



					def 	_lowercase					( self					:  Dict	):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				List[str]       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				Tuple       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
							with self.assertRaises(__A	):
								snake_case__			:				int       =  FeaturesManager.determine_framework(__A	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	):
							snake_case__			:				List[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						snake_case__			:				Tuple       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				int       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Both in environment -> use PyTorch
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[int]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# Both not in environment -> raise error
						snake_case__			:				List[str]       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[Any]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							with self.assertRaises(__A	):
								snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)



