


'''simple docstring'''
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    Adafactor,
							    AdamW,
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_inverse_sqrt_schedule,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0 ):
							A_			    =		[]
							for _ in range(UpperCamelCase_ ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
							return lrs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0 ):
							A_			    =		[]
							for step in range(UpperCamelCase_ ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												A_			    =		os.path.join(UpperCamelCase_						,  "schedule.bin" )
																												torch.save(scheduler.state_dict()						,  UpperCamelCase_ )

																												A_			    =		torch.load(UpperCamelCase_ )
																												scheduler.load_state_dict(UpperCamelCase_ )
							return lrs






@require_torch
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Union[str, Any]	):
													self.assertEqual(len(lowerCAmelCase	) ,  len(lowerCAmelCase	)	)
													for a, b in zip(lowerCAmelCase ,  lowerCAmelCase	):
																				self.assertAlmostEqual(lowerCAmelCase ,  lowerCAmelCase ,  delta=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		torch.tensor([0.1, -0.2, -0.1] ,  requires_grad=lowerCAmelCase	)
													A_			    =		torch.tensor([0.4, 0.2, -0.5]	)
													A_			    =		nn.MSELoss()
													# No warmup, constant schedule, no gradient clipping
													A_			    =		AdamW(params=[w] ,  lr=2e-1 ,  weight_decay=0.0	)
													for _ in range(1_00	):
																				A_			    =		criterion(lowerCAmelCase ,  lowerCAmelCase	)
																				loss.backward()
																				optimizer.step()
																				w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																				w.grad.zero_()
													self.assertListAlmostEqual(w.tolist() ,  [0.4, 0.2, -0.5] ,  tol=1e-2	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		torch.tensor([0.1, -0.2, -0.1] ,  requires_grad=lowerCAmelCase	)
													A_			    =		torch.tensor([0.4, 0.2, -0.5]	)
													A_			    =		nn.MSELoss()
													# No warmup, constant schedule, no gradient clipping
													A_			    =		Adafactor(
													    params=[w] ,  lr=1e-2 ,  eps=(1e-30, 1e-3) ,  clip_threshold=1.0 ,  decay_rate=-0.8 ,  betaa=lowerCAmelCase ,  weight_decay=0.0 ,  relative_step=lowerCAmelCase ,  scale_parameter=lowerCAmelCase ,  warmup_init=lowerCAmelCase ,  )
													for _ in range(10_00	):
																				A_			    =		criterion(lowerCAmelCase ,  lowerCAmelCase	)
																				loss.backward()
																				optimizer.step()
																				w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																				w.grad.zero_()
													self.assertListAlmostEqual(w.tolist() ,  [0.4, 0.2, -0.5] ,  tol=1e-2	)






@require_torch
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=nn.Linear(50				,			50						) if is_torch_available() else None
						_UpperCAmelCase		:       Optional[Any]   						=AdamW(m.parameters()				,			lr=1_0.0						) if is_torch_available() else None
						_UpperCAmelCase		:       Dict   						=10






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Optional[int]=None	):
													self.assertEqual(len(lowerCAmelCase	) ,  len(lowerCAmelCase	)	)
													for a, b in zip(lowerCAmelCase ,  lowerCAmelCase	):
																				self.assertAlmostEqual(lowerCAmelCase ,  lowerCAmelCase ,  delta=lowerCAmelCase ,  msg=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		{"num_warmup_steps": 2, "num_training_steps": 10}
													# schedulers doct format
													# function: (sched_args_dict, expected_learning_rates)
													A_			    =		{
													    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
													    get_constant_schedule_with_warmup: (
													        {"num_warmup_steps": 4},
													        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
													    ),
													    get_linear_schedule_with_warmup: (
													        {**common_kwargs},
													        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
													    ),
													    get_cosine_schedule_with_warmup: (
													        {**common_kwargs},
													        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
													    ),
													    get_cosine_with_hard_restarts_schedule_with_warmup: (
													        {**common_kwargs, "num_cycles": 2},
													        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
													    ),
													    get_polynomial_decay_schedule_with_warmup: (
													        {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
													        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
													    ),
													    get_inverse_sqrt_schedule: (
													        {"num_warmup_steps": 2},
													        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
													    ),
													}

													for scheduler_func, data in scheds.items():
																				A_    ,					A_			    =		data

																				A_			    =		scheduler_func(self.optimizer ,  **lowerCAmelCase	)
																				self.assertEqual(len([scheduler.get_lr()[0]]	) ,  1	)
																				A_			    =		unwrap_schedule(lowerCAmelCase ,  self.num_steps	)
																				self.assertListAlmostEqual(
																				    lowerCAmelCase ,  lowerCAmelCase ,  tol=1e-2 ,  msg=F"failed for {scheduler_func} in normal scheduler" ,  )

																				A_			    =		scheduler_func(self.optimizer ,  **lowerCAmelCase	)
																				if scheduler_func.__name__ != "get_constant_schedule":
																											LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase	)  # wrap to test picklability of the schedule
																				A_			    =		unwrap_and_save_reload_schedule(lowerCAmelCase ,  self.num_steps	)
																				self.assertListEqual(lowerCAmelCase ,  lowerCAmelCase ,  msg=F"failed for {scheduler_func} in save and reload"	)






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		fn






						def __call__(  self							:      Any ,  *lowerCAmelCase							:      int ,  **lowerCAmelCase							:      int	):
													return self.fn(*lowerCAmelCase ,  **lowerCAmelCase	)






						@classmethod
						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Tuple	):
													A_			    =		list(map(self ,  scheduler.lr_lambdas	)	)









'''simple docstring'''
from __future__ import annotations

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

__SCREAMING_SNAKE_CASE					:				Union[str, 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],
]

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






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													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()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													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 				_UpperCAmelCase       (  self							:      str	):
													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:
																											A_			    =		True
																											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	)

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






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													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 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													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


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





