"""simple docstring"""


from __future__ import annotations

UpperCAmelCase__  		=						list[tuple[int, int]]

UpperCAmelCase__  		=						[
    [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],
]

UpperCAmelCase__  		=						([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class        a				:


				def __init__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  float   ,				__lowerCAmelCase	:  Node | None   ,				):
						_UpperCAmelCase						 =      pos_x
						_UpperCAmelCase						 =      pos_y
						_UpperCAmelCase						 =      (pos_y, pos_x)
						_UpperCAmelCase						 =      goal_x
						_UpperCAmelCase						 =      goal_y
						_UpperCAmelCase						 =      g_cost
						_UpperCAmelCase						 =      parent
						_UpperCAmelCase						 =      self.calculate_heuristic()


				def      lowerCAmelCase_  (					self	:  List[Any]					):
						_UpperCAmelCase						 =      abs(self.pos_x - self.goal_x					)
						_UpperCAmelCase						 =      abs(self.pos_y - self.goal_y					)
						return dx + dy


				def __lt__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict					):
						return self.f_cost < other.f_cost



class        a				:


				def __init__(					self	:  List[str]   ,				__lowerCAmelCase	:  tuple[int, int]   ,				__lowerCAmelCase	:  tuple[int, int]					):
						_UpperCAmelCase						 =      Node(start[1]   ,				start[0]   ,				goal[1]   ,				goal[0]   ,				0   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      Node(goal[1]   ,				goal[0]   ,				goal[1]   ,				goal[0]   ,				9_9999   ,				__lowerCAmelCase					)

						_UpperCAmelCase						 =      [self.start]
						_UpperCAmelCase						 =      []

						_UpperCAmelCase						 =      False


				def      lowerCAmelCase_  (					self	:  List[str]					):
						while self.open_nodes:
								# Open Nodes are sorted using __lt__
								self.open_nodes.sort()
								_UpperCAmelCase						 =      self.open_nodes.pop(0					)

								if current_node.pos == self.target.pos:
										_UpperCAmelCase						 =      True
										return self.retrace_path(__lowerCAmelCase					)

								self.closed_nodes.append(__lowerCAmelCase					)
								_UpperCAmelCase						 =      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
												_UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Node					):
						_UpperCAmelCase						 =      []
						for action in delta:
								_UpperCAmelCase						 =      parent.pos_x + action[1]
								_UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Node | None					):
						_UpperCAmelCase						 =      node
						_UpperCAmelCase						 =      []
						while current_node is not None:
								path.append((current_node.pos_y, current_node.pos_x)					)
								_UpperCAmelCase						 =      current_node.parent
						path.reverse()
						return path


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

				print("""------""")

				UpperCAmelCase__  		=						GreedyBestFirst(init, goal)
				UpperCAmelCase__  		=						greedy_bf.search()
				if path:
								for pos_x, pos_y in path:
												UpperCAmelCase__  		=						2

								for elem in grid:
												print(elem)

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    UpperCAmelCase__  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
