





"""simple docstring"""




from __future__ import annotations

import time

a		:				Optional[int]  			=    list[tuple[int, int]]

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


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Any:
								a   :	Dict			=       pos_x
								a   :	Tuple			=       pos_y
								a   :	Optional[int]			=       (pos_y, pos_x)
								a   :	Optional[int]			=       goal_x
								a   :	List[Any]			=       goal_y
								a   :	Dict			=       parent


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								a   :	Any			=       Node(start[1]   ,      start[0]   ,      goal[1]   ,      goal[0]   ,      _a   )
								a   :	Tuple			=       Node(goal[1]   ,      goal[0]   ,      goal[1]   ,      goal[0]   ,      _a   )

								a   :	Union[str, Any]			=       [self.start]
								a   :	Union[str, Any]			=       False






							def 					__a     (     self   )			->				Path | None:


								while self.node_queue:
									a   :	str			=       self.node_queue.pop(0   )

									if current_node.pos == self.target.pos:
										a   :	Tuple			=       True
										return self.retrace_path(_a   )

									a   :	Dict			=       self.get_successors(_a   )

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

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






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				list[Node]:
								a   :	Union[str, Any]			=       []
								for action in delta:
									a   :	Union[str, Any]			=       parent.pos_x + action[1]
									a   :	Optional[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   ,      _a   )   )
								return successors






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Path:
								a   :	Union[str, Any]			=       node
								a   :	int			=       []
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x)   )
									a   :	Optional[int]			=       current_node.parent
								path.reverse()
								return path


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								a   :	List[Any]			=       BreadthFirstSearch(_a   ,      _a   )
								a   :	Union[str, Any]			=       BreadthFirstSearch(_a   ,      _a   )
								a   :	List[str]			=       False






							def 					__a     (     self   )			->				Path | None:


								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									a   :	str			=       self.fwd_bfs.node_queue.pop(0   )
									a   :	str			=       self.bwd_bfs.node_queue.pop(0   )

									if current_bwd_node.pos == current_fwd_node.pos:
										a   :	List[str]			=       True
										return self.retrace_bidirectional_path(
										    _a   ,      _a   )

									a   :	Union[str, Any]			=       current_bwd_node
									a   :	Tuple			=       current_fwd_node

									a   :	List[Any]			=       {
									    self.fwd_bfs: self.fwd_bfs.get_successors(_a   ),
									    self.bwd_bfs: self.bwd_bfs.get_successors(_a   ),
									}

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

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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Path:
								a   :	int			=       self.fwd_bfs.retrace_path(_a   )
								a   :	List[Any]			=       self.bwd_bfs.retrace_path(_a   )
								bwd_path.pop()
								bwd_path.reverse()
								a   :	Optional[int]			=       fwd_path + bwd_path
								return path


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

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

		a		:				Dict  			=    time.time()
		a		:				Any  			=    BreadthFirstSearch(init, goal)
		a		:				str  			=    bfs.search()
		a		:				Union[str, Any]  			=    time.time() - start_bfs_time

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

		a		:				Union[str, Any]  			=    time.time()
		a		:				int  			=    BidirectionalBreadthFirstSearch(init, goal)
		a		:				int  			=    bd_bfs.search()
		a		:				int  			=    time.time() - start_bd_bfs_time

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








"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


