




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowerCamelCase	:	List[str]				=   0

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

lowerCamelCase	:	Dict				=   tuple[int, int]




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    None:
         snake_case				:    Any           =	pos_x
         snake_case				:    Tuple           =	pos_y
         snake_case				:    int           =	(pos_y, pos_x)
         snake_case				:    Dict           =	goal_x
         snake_case				:    int           =	goal_y
         snake_case				:    Any           =	g_cost
         snake_case				:    Any           =	parent
         snake_case				:    Tuple           =	self.calculate_heuristic()
         snake_case				:    List[str]           =	self.g_cost + self.h_cost

      def      UpperCAmelCase				(				self     )		->    float:
         snake_case				:    Optional[int]           =	self.pos_x - self.goal_x
         snake_case				:    Optional[Any]           =	self.pos_y - self.goal_y
         if HEURISTIC == 1:
            return abs(A     ) + abs(A     )
         else:
            return sqrt(dy**2 + dx**2     )

      def __lt__(				self    ,				A     )		->    bool:
         return self.f_cost < other.f_cost




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A     )		->    Optional[int]:
         snake_case				:    List[str]           =	Node(start[1]    ,				start[0]    ,				goal[1]    ,				goal[0]    ,				0    ,				A     )
         snake_case				:    Optional[Any]           =	Node(goal[1]    ,				goal[0]    ,				goal[1]    ,				goal[0]    ,				9_9_9_9_9    ,				A     )

         snake_case				:    Optional[int]           =	[self.start]
         snake_case				:    list[Node]           =	[]

         snake_case				:    Tuple           =	False

      def      UpperCAmelCase				(				self     )		->    list[TPosition]:
         while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            snake_case				:    List[str]           =	self.open_nodes.pop(0     )

            if current_node.pos == self.target.pos:
               return self.retrace_path(A     )

            self.closed_nodes.append(A     )
            snake_case				:    Dict           =	self.get_successors(A     )

            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(A     )
               else:
                  # retrieve the best current path
                  snake_case				:    Tuple           =	self.open_nodes.pop(self.open_nodes.index(A     )     )

                  if child_node.g_cost < better_node.g_cost:
                     self.open_nodes.append(A     )
                  else:
                     self.open_nodes.append(A     )

         return [self.start.pos]

      def      UpperCAmelCase				(				self    ,				A     )		->    list[Node]:
         snake_case				:    Optional[Any]           =	[]
         for action in delta:
            snake_case				:    Union[str, Any]           =	parent.pos_x + action[1]
            snake_case				:    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    ,				parent.g_cost + 1    ,				A    ,				)     )
         return successors

      def      UpperCAmelCase				(				self    ,				A     )		->    list[TPosition]:
         snake_case				:    Optional[Any]           =	node
         snake_case				:    Dict           =	[]
         while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)     )
            snake_case				:    Dict           =	current_node.parent
         path.reverse()
         return path




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A     )		->    None:
         snake_case				:    Dict           =	AStar(A    ,				A     )
         snake_case				:    Optional[int]           =	AStar(A    ,				A     )
         snake_case				:    int           =	False

      def      UpperCAmelCase				(				self     )		->    list[TPosition]:
         while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
            self.fwd_astar.open_nodes.sort()
            self.bwd_astar.open_nodes.sort()
            snake_case				:    Dict           =	self.fwd_astar.open_nodes.pop(0     )
            snake_case				:    Tuple           =	self.bwd_astar.open_nodes.pop(0     )

            if current_bwd_node.pos == current_fwd_node.pos:
               return self.retrace_bidirectional_path(
                   A    ,				A     )

            self.fwd_astar.closed_nodes.append(A     )
            self.bwd_astar.closed_nodes.append(A     )

            snake_case				:    str           =	current_bwd_node
            snake_case				:    Optional[Any]           =	current_fwd_node

            snake_case				:    str           =	{
                self.fwd_astar: self.fwd_astar.get_successors(A     ),
                self.bwd_astar: self.bwd_astar.get_successors(A     ),
            }

            for astar in [self.fwd_astar, self.bwd_astar]:
               for child_node in successors[astar]:
                  if child_node in astar.closed_nodes:
                     continue

                  if child_node not in astar.open_nodes:
                     astar.open_nodes.append(A     )
                  else:
                     # retrieve the best current path
                     snake_case				:    Dict           =	astar.open_nodes.pop(
                         astar.open_nodes.index(A     )     )

                     if child_node.g_cost < better_node.g_cost:
                        astar.open_nodes.append(A     )
                     else:
                        astar.open_nodes.append(A     )

         return [self.fwd_astar.start.pos]

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    list[TPosition]:
         snake_case				:    List[str]           =	self.fwd_astar.retrace_path(A     )
         snake_case				:    Optional[int]           =	self.bwd_astar.retrace_path(A     )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case				:    str           =	fwd_path + bwd_path
         return path


if __name__ == "__main__":
       # all coordinates are given in format [y,x]
       lowerCamelCase	:	str				=   (0, 0)
       lowerCamelCase	:	Union[str, Any]				=   (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       lowerCamelCase	:	int				=   time.time()
       lowerCamelCase	:	Optional[int]				=   AStar(init, goal)
       lowerCamelCase	:	str				=   a_star.search()
       lowerCamelCase	:	List[str]				=   time.time() - start_time
       print(f"""AStar execution time = {end_time:f} seconds""")

       lowerCamelCase	:	Any				=   time.time()
       lowerCamelCase	:	int				=   BidirectionalAStar(init, goal)
       lowerCamelCase	:	Any				=   time.time() - bd_start_time
       print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")











import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase	:	Optional[Any]				=   logging.get_logger(__name__)

lowerCamelCase	:	Optional[int]				=   {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""bart"""
      _snake_case												=						["""past_key_values"""]
      _snake_case												=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

      def __init__(				self    ,				A=5_0_2_6_5    ,				A=1_0_2_4    ,				A=1_2    ,				A=4_0_9_6    ,				A=1_6    ,				A=1_2    ,				A=4_0_9_6    ,				A=1_6    ,				A=0.0    ,				A=0.0    ,				A="gelu"    ,				A=1_0_2_4    ,				A=0.1    ,				A=0.0    ,				A=0.0    ,				A=0.02    ,				A=0.0    ,				A=False    ,				A=True    ,				A=3    ,				A=1    ,				A=0    ,				A=2    ,				A=True    ,				A=2    ,				A=2    ,				**A    ,				)		->    Any:
         snake_case				:    Optional[int]           =	vocab_size
         snake_case				:    Union[str, Any]           =	max_position_embeddings
         snake_case				:    List[str]           =	d_model
         snake_case				:    List[Any]           =	encoder_ffn_dim
         snake_case				:    Optional[Any]           =	encoder_layers
         snake_case				:    Union[str, Any]           =	encoder_attention_heads
         snake_case				:    str           =	decoder_ffn_dim
         snake_case				:    Union[str, Any]           =	decoder_layers
         snake_case				:    Any           =	decoder_attention_heads
         snake_case				:    Union[str, Any]           =	dropout
         snake_case				:    List[str]           =	attention_dropout
         snake_case				:    List[Any]           =	activation_dropout
         snake_case				:    Optional[int]           =	activation_function
         snake_case				:    Union[str, Any]           =	init_std
         snake_case				:    List[str]           =	encoder_layerdrop
         snake_case				:    int           =	decoder_layerdrop
         snake_case				:    str           =	classifier_dropout
         snake_case				:    List[str]           =	use_cache
         snake_case				:    Tuple           =	encoder_layers
         snake_case				:    int           =	scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=A    ,				pad_token_id=A    ,				bos_token_id=A    ,				eos_token_id=A    ,				is_encoder_decoder=A    ,				decoder_start_token_id=A    ,				forced_eos_token_id=A    ,				**A    ,				)

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""    ,				A     ):
            snake_case				:    Any           =	self.bos_token_id
            warnings.warn(
                f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                """The config can simply be saved and uploaded again to be fixed."""     )




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      @property
      def      UpperCAmelCase				(				self     )		->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            snake_case				:    Optional[Any]           =	OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ]     )

            if self.use_past:
               snake_case				:    Tuple           =	{0: """batch"""}
               snake_case				:    List[Any]           =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
            else:
               snake_case				:    Union[str, Any]           =	{0: """batch""", 1: """decoder_sequence"""}
               snake_case				:    Any           =	{0: """batch""", 1: """decoder_sequence"""}

            if self.use_past:
               self.fill_with_past_key_values_(A    ,				direction="""inputs"""     )
         elif self.task == "causal-lm":
            # TODO: figure this case out.
            snake_case				:    Union[str, Any]           =	OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ]     )
            if self.use_past:
               snake_case     , snake_case				:    List[Any]           =	self.num_layers
               for i in range(A     ):
                  snake_case				:    List[Any]           =	{0: """batch""", 2: """past_sequence + sequence"""}
                  snake_case				:    Optional[int]           =	{0: """batch""", 2: """past_sequence + sequence"""}
         else:
            snake_case				:    Union[str, Any]           =	OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                    ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                ]     )

         return common_inputs

      @property
      def      UpperCAmelCase				(				self     )		->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            snake_case				:    Any           =	super().outputs
         else:
            snake_case				:    Any           =	super(A    ,				self     ).outputs
            if self.use_past:
               snake_case     , snake_case				:    Any           =	self.num_layers
               for i in range(A     ):
                  snake_case				:    Any           =	{0: """batch""", 2: """past_sequence + sequence"""}
                  snake_case				:    Union[str, Any]           =	{0: """batch""", 2: """past_sequence + sequence"""}
         return common_outputs

      def      UpperCAmelCase				(				self    ,				A    ,				A = -1    ,				A = -1    ,				A = False    ,				A = None    ,				)		->    Mapping[str, Any]:
         snake_case				:    Optional[Any]           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A    ,				A    ,				A    ,				A    ,				A     )

         # Generate decoder inputs
         snake_case				:    Any           =	seq_length if not self.use_past else 1
         snake_case				:    List[str]           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A    ,				A    ,				A    ,				A    ,				A     )
         snake_case				:    Optional[int]           =	{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         snake_case				:    List[str]           =	dict(**A    ,				**A     )

         if self.use_past:
            if not is_torch_available():
               raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
            else:
               import torch
            snake_case     , snake_case				:    Optional[int]           =	common_inputs["""input_ids"""].shape
            snake_case				:    Any           =	common_inputs["""decoder_input_ids"""].shape[1]
            snake_case     , snake_case				:    Optional[Any]           =	self.num_attention_heads
            snake_case				:    Optional[int]           =	(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            snake_case				:    Any           =	decoder_seq_length + 3
            snake_case				:    List[Any]           =	(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            snake_case				:    str           =	torch.cat(
                [common_inputs["""decoder_attention_mask"""], torch.ones(A    ,				A     )]    ,				dim=1     )

            snake_case				:    str           =	[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            snake_case     , snake_case				:    Any           =	self.num_layers
            snake_case				:    List[str]           =	min(A    ,				A     )
            snake_case				:    Dict           =	max(A    ,				A     ) - min_num_layers
            snake_case				:    List[str]           =	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

            for _ in range(A     ):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(A     ),
                       torch.zeros(A     ),
                       torch.zeros(A     ),
                       torch.zeros(A     ),
                   )     )
            # TODO: test this.
            snake_case				:    Tuple           =	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
            for _ in range(A    ,				A     ):
               common_inputs["past_key_values"].append((torch.zeros(A     ), torch.zeros(A     ))     )
         return common_inputs

      def      UpperCAmelCase				(				self    ,				A    ,				A = -1    ,				A = -1    ,				A = False    ,				A = None    ,				)		->    Mapping[str, Any]:
         snake_case				:    str           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A    ,				A    ,				A    ,				A    ,				A     )

         if self.use_past:
            if not is_torch_available():
               raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
            else:
               import torch
            snake_case     , snake_case				:    str           =	common_inputs["""input_ids"""].shape
            # Not using the same length for past_key_values
            snake_case				:    Optional[int]           =	seqlen + 2
            snake_case     , snake_case				:    Tuple           =	self.num_layers
            snake_case     , snake_case				:    Optional[Any]           =	self.num_attention_heads
            snake_case				:    Union[str, Any]           =	(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            snake_case				:    Optional[Any]           =	common_inputs["""attention_mask"""].dtype
            snake_case				:    int           =	torch.cat(
                [common_inputs["""attention_mask"""], torch.ones(A    ,				A    ,				dtype=A     )]    ,				dim=1     )
            snake_case				:    Union[str, Any]           =	[
                (torch.zeros(A     ), torch.zeros(A     )) for _ in range(A     )
            ]
         return common_inputs

      def      UpperCAmelCase				(				self    ,				A    ,				A = -1    ,				A = -1    ,				A = False    ,				A = None    ,				)		->    Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         snake_case				:    int           =	compute_effective_axis_dimension(
             A    ,				fixed_dimension=OnnxConfig.default_fixed_batch    ,				num_token_to_add=0     )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         snake_case				:    int           =	tokenizer.num_special_tokens_to_add(A     )
         snake_case				:    Tuple           =	compute_effective_axis_dimension(
             A    ,				fixed_dimension=OnnxConfig.default_fixed_sequence    ,				num_token_to_add=A     )

         # Generate dummy inputs according to compute batch and sequence
         snake_case				:    int           =	[""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
         snake_case				:    str           =	dict(tokenizer(A    ,				return_tensors=A     )     )
         return common_inputs

      def      UpperCAmelCase				(				self    ,				A    ,				A = -1    ,				A = -1    ,				A = False    ,				A = None    ,				)		->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
            snake_case				:    Optional[Any]           =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                A    ,				batch_size=A    ,				seq_length=A    ,				is_pair=A    ,				framework=A     )

         elif self.task == "causal-lm":
            snake_case				:    Optional[int]           =	self._generate_dummy_inputs_for_causal_lm(
                A    ,				batch_size=A    ,				seq_length=A    ,				is_pair=A    ,				framework=A     )
         else:
            snake_case				:    List[Any]           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                A    ,				batch_size=A    ,				seq_length=A    ,				is_pair=A    ,				framework=A     )

         return common_inputs

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A     )		->    Union[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
            snake_case				:    Optional[Any]           =	super()._flatten_past_key_values_(A    ,				A    ,				A    ,				A     )
         else:
            snake_case				:    Union[str, Any]           =	super(A    ,				self     )._flatten_past_key_values_(
                A    ,				A    ,				A    ,				A     )





