"""simple docstring"""







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
						from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
						from tensorflow.keras.optimizers import Adam



class  _lowerCamelCase			(tf.keras.optimizers.schedules.LearningRateSchedule     ):







				def __init__( self    :       List[Any]			,					lowerCamelCase_    :       float			,					lowerCamelCase_    :       Callable			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       float = 1.0			,					lowerCamelCase_    :       str = None			,					):

					"""simple docstring"""






					super().__init__()
					_lowercase :    Dict							=							initial_learning_rate
					_lowercase :    List[Any]							=							warmup_steps
					_lowercase :    Tuple							=							power
					_lowercase :    int							=							decay_schedule_fn
					_lowercase :    Any							=							name







				def __call__( self    :       List[str]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					with tf.name_scope(self.name or 'WarmUp'						) as name:
						# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
						# learning rate will be `global_step/num_warmup_steps * init_lr`.
						_lowercase :    Optional[Any]							=							tf.cast(lowerCamelCase_			,					tf.floataa						)
						_lowercase :    Optional[Any]							=							tf.cast(self.warmup_steps			,					tf.floataa						)
						_lowercase :    List[str]							=							global_step_float / warmup_steps_float
						_lowercase :    Dict							=							self.initial_learning_rate * tf.math.pow(lowerCamelCase_			,					self.power						)
						return tf.cond(
						    global_step_float < warmup_steps_float			,					lambda: warmup_learning_rate			,					lambda: self.decay_schedule_fn(step - self.warmup_steps						)			,					name=lowerCamelCase_			,					)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return {
					    "initial_learning_rate": self.initial_learning_rate,
					    "decay_schedule_fn": self.decay_schedule_fn,
					    "warmup_steps": self.warmup_steps,
					    "power": self.power,
					    "name": self.name,
					}



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = 0.0     ,__UpperCAmelCase = 0.9     ,__UpperCAmelCase = 0.9_9_9     ,__UpperCAmelCase = 1E-8     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = 0.0     ,__UpperCAmelCase = 1.0     ,__UpperCAmelCase = None     ,):






	"""simple docstring"""





	_lowercase :    str							=							tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=__UpperCAmelCase     ,decay_steps=num_train_steps - num_warmup_steps     ,end_learning_rate=init_lr * min_lr_ratio     ,power=__UpperCAmelCase     ,)
	if num_warmup_steps:
		_lowercase :    List[str]							=							WarmUp(
		    initial_learning_rate=__UpperCAmelCase     ,decay_schedule_fn=__UpperCAmelCase     ,warmup_steps=__UpperCAmelCase     ,)
	if weight_decay_rate > 0.0:
		_lowercase :    Union[str, Any]							=							AdamWeightDecay(
		    learning_rate=__UpperCAmelCase     ,weight_decay_rate=__UpperCAmelCase     ,beta_a=__UpperCAmelCase     ,beta_a=__UpperCAmelCase     ,epsilon=__UpperCAmelCase     ,clipnorm=__UpperCAmelCase     ,global_clipnorm=__UpperCAmelCase     ,exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']     ,include_in_weight_decay=__UpperCAmelCase     ,)
	else:
		_lowercase :    Optional[int]							=							tf.keras.optimizers.Adam(
		    learning_rate=__UpperCAmelCase     ,beta_a=__UpperCAmelCase     ,beta_a=__UpperCAmelCase     ,epsilon=__UpperCAmelCase     ,clipnorm=__UpperCAmelCase     ,global_clipnorm=__UpperCAmelCase     ,)
	# We return the optimizer and the LR scheduler in order to better track the
	# evolution of the LR independently of the optimizer.
	return optimizer, lr_schedule



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Dict			,					lowerCamelCase_    :       Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001			,					lowerCamelCase_    :       float = 0.9			,					lowerCamelCase_    :       float = 0.999			,					lowerCamelCase_    :       float = 1E-7			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       float = 0.0			,					lowerCamelCase_    :       Optional[List[str]] = None			,					lowerCamelCase_    :       Optional[List[str]] = None			,					lowerCamelCase_    :       str = "AdamWeightDecay"			,					**lowerCamelCase_    :       int			,					):

					"""simple docstring"""






					super().__init__(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					**lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							weight_decay_rate
					_lowercase :    str							=							include_in_weight_decay
					_lowercase :    Dict							=							exclude_from_weight_decay







				@classmethod
				def 							__UpperCAmelCase					( cls    :       Union[str, Any]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    Tuple							=							{'WarmUp': WarmUp}
					return super(lowerCamelCase_			,					cls						).from_config(lowerCamelCase_			,					custom_objects=lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					super(lowerCamelCase_			,					self						)._prepare_local(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							tf.constant(
					    self.weight_decay_rate			,					name='adam_weight_decay_rate'						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					_lowercase :    Any							=							self._do_use_weight_decay(var.name						)
					if do_decay:
						return var.assign_sub(
						    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']			,					use_locking=self._use_locking			,					)
					return tf.no_op()







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Any=None			,					**lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    int							=							list(zip(*lowerCamelCase_						)						)
					return super(lowerCamelCase_			,					self						).apply_gradients(zip(lowerCamelCase_			,					lowerCamelCase_						)			,					name=lowerCamelCase_			,					**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					if apply_state is None:
						return self._decayed_lr_t[var_dtype], {}

					_lowercase :    int							=							apply_state or {}
					_lowercase :    List[Any]							=							apply_state.get((var_device, var_dtype)						)
					if coefficients is None:
						_lowercase :    Any							=							self._fallback_apply_state(lowerCamelCase_			,					lowerCamelCase_						)
						_lowercase :    Union[str, Any]							=							coefficients

					return coefficients["lr_t"], {"apply_state": apply_state}







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]=None						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    List[Any]							=							self._get_lr(var.device			,					var.dtype.base_dtype			,					lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							self._decay_weights_op(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)
					with tf.control_dependencies([decay]						):
						return super(lowerCamelCase_			,					self						)._resource_apply_dense(lowerCamelCase_			,					lowerCamelCase_			,					**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, Any]=None						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    Any							=							self._get_lr(var.device			,					var.dtype.base_dtype			,					lowerCamelCase_						)
					_lowercase :    List[Any]							=							self._decay_weights_op(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)
					with tf.control_dependencies([decay]						):
						return super(lowerCamelCase_			,					self						)._resource_apply_sparse(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							super().get_config()
					config.update({'weight_decay_rate': self.weight_decay_rate}						)
					return config







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






					if self.weight_decay_rate == 0:
						return False

					if self._include_in_weight_decay:
						for r in self._include_in_weight_decay:
							if re.search(lowerCamelCase_			,					lowerCamelCase_						) is not None:
								return True

					if self._exclude_from_weight_decay:
						for r in self._exclude_from_weight_decay:
							if re.search(lowerCamelCase_			,					lowerCamelCase_						) is not None:
								return False
					return True



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Any							=							[]
					_lowercase :    List[Any]							=							None







				@property
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					if self._accum_steps is None:
						_lowercase :    List[str]							=							tf.Variable(
						    tf.constant(0			,					dtype=tf.intaa						)			,					trainable=lowerCamelCase_			,					synchronization=tf.VariableSynchronization.ON_READ			,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,					)

					return self._accum_steps.value()







				@property
				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					if not self._gradients:
						raise ValueError('The accumulator should be called first to initialize the gradients'						)
					return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]







				def __call__( self    :       List[Any]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					if not self._gradients:
						_lowercase :    Optional[int]							=							self.step  # Create the step variable.
						self._gradients.extend(
						    [
						        tf.Variable(
						            tf.zeros_like(lowerCamelCase_						)			,					trainable=lowerCamelCase_			,					synchronization=tf.VariableSynchronization.ON_READ			,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,					)
						        if gradient is not None
						        else gradient
						        for gradient in gradients
						    ]						)
					if len(lowerCamelCase_						) != len(self._gradients						):
						raise ValueError(F'''Expected {len(self._gradients						)} gradients, but got {len(lowerCamelCase_						)}'''						)

					for accum_gradient, gradient in zip(self._gradients			,					lowerCamelCase_						):
						if accum_gradient is not None and gradient is not None:
							accum_gradient.assign_add(lowerCamelCase_						)

					self._accum_steps.assign_add(1						)







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					if not self._gradients:
						return
					self._accum_steps.assign(0						)
					for gradient in self._gradients:
						if gradient is not None:
							gradient.assign(tf.zeros_like(lowerCamelCase_						)						)




"""simple docstring"""







from __future__ import annotations

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



