




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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)

snake_case_ : Optional[Any]    		=       {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  '''layoutlmv3'''







   def __init__(      self		:						Optional[int]					,						_snake_case		:						Dict=50265					,						_snake_case		:						Optional[int]=768					,						_snake_case		:						Optional[Any]=12					,						_snake_case		:						List[Any]=12					,						_snake_case		:						Tuple=3072					,						_snake_case		:						Dict="gelu"					,						_snake_case		:						Optional[int]=0.1					,						_snake_case		:						Optional[int]=0.1					,						_snake_case		:						List[str]=512					,						_snake_case		:						List[str]=2					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						str=1e-5					,						_snake_case		:						Tuple=1					,						_snake_case		:						Dict=0					,						_snake_case		:						Union[str, Any]=2					,						_snake_case		:						Optional[Any]=1024					,						_snake_case		:						Tuple=128					,						_snake_case		:						int=128					,						_snake_case		:						List[str]=True					,						_snake_case		:						Union[str, Any]=32					,						_snake_case		:						Optional[int]=128					,						_snake_case		:						Any=64					,						_snake_case		:						List[str]=256					,						_snake_case		:						List[str]=True					,						_snake_case		:						Any=True					,						_snake_case		:						str=True					,						_snake_case		:						Optional[int]=224					,						_snake_case		:						Dict=3					,						_snake_case		:						str=16					,						_snake_case		:						List[Any]=None					,						**_snake_case		:						Dict					,						):

        """simple docstring"""


        super().__init__(
            vocab_size=_snake_case					,						hidden_size=_snake_case					,						num_hidden_layers=_snake_case					,						num_attention_heads=_snake_case					,						intermediate_size=_snake_case					,						hidden_act=_snake_case					,						hidden_dropout_prob=_snake_case					,						attention_probs_dropout_prob=_snake_case					,						max_position_embeddings=_snake_case					,						type_vocab_size=_snake_case					,						initializer_range=_snake_case					,						layer_norm_eps=_snake_case					,						pad_token_id=_snake_case					,						bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						**_snake_case					,						)
        UpperCAmelCase_          =							max_ad_position_embeddings
        UpperCAmelCase_          =							coordinate_size
        UpperCAmelCase_          =							shape_size
        UpperCAmelCase_          =							has_relative_attention_bias
        UpperCAmelCase_          =							rel_pos_bins
        UpperCAmelCase_          =							max_rel_pos
        UpperCAmelCase_          =							has_spatial_attention_bias
        UpperCAmelCase_          =							rel_ad_pos_bins
        UpperCAmelCase_          =							max_rel_ad_pos
        UpperCAmelCase_          =							text_embed
        UpperCAmelCase_          =							visual_embed
        UpperCAmelCase_          =							input_size
        UpperCAmelCase_          =							num_channels
        UpperCAmelCase_          =							patch_size
        UpperCAmelCase_          =							classifier_dropout







class __snake_case			(			a		):
   UpperCAmelCase__  :    int          =  version.parse('''1.12'''		)







   @property
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        if self.task in ["question-answering", "sequence-classification"]:
             return OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                     ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ])
        else:
             return OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                     ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                 ])







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return 1e-5







   @property
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        return 12







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						"ProcessorMixin"					,						_snake_case		:						int = -1					,						_snake_case		:						int = -1					,						_snake_case		:						bool = False					,						_snake_case		:						Optional["TensorType"] = None					,						_snake_case		:						int = 3					,						_snake_case		:						int = 40					,						_snake_case		:						int = 40					,						):

        """simple docstring"""


        setattr(processor.image_processor					,						'''apply_ocr'''					,						_snake_case)

        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        UpperCAmelCase_          =							compute_effective_axis_dimension(
            _snake_case					,						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
        UpperCAmelCase_          =							processor.tokenizer.num_special_tokens_to_add(_snake_case)
        UpperCAmelCase_          =							compute_effective_axis_dimension(
            _snake_case					,						fixed_dimension=OnnxConfig.default_fixed_sequence					,						num_token_to_add=_snake_case)
        # Generate dummy inputs according to compute batch and sequence
        UpperCAmelCase_          =							[[''' '''.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

        # Generate dummy bounding boxes
        UpperCAmelCase_          =							[[[48, 84, 73, 128]]] * batch_size

        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
        UpperCAmelCase_          =							self._generate_dummy_images(_snake_case					,						_snake_case					,						_snake_case					,						_snake_case)

        UpperCAmelCase_          =							dict(
            processor(
                _snake_case					,						text=_snake_case					,						boxes=_snake_case					,						return_tensors=_snake_case					,						))

        return inputs












from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
snake_case_ : str    		=       0

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],
]

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

snake_case_ : List[Any]    		=       tuple[int, int]







class __snake_case			:







   def __init__(      self		:						Any					,						_snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						Node | None					,						):

        """simple docstring"""


        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()
        UpperCAmelCase_          =							self.g_cost + self.h_cost







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.pos_x - self.goal_x
        UpperCAmelCase_          =							self.pos_y - self.goal_y
        if HEURISTIC == 1:
             return abs(_snake_case) + abs(_snake_case)
        else:
             return sqrt(dy**2 + dx**2)







   def __lt__(      self		:						Union[str, Any]					,						_snake_case		:						Node):

        """simple docstring"""


        return self.f_cost < other.f_cost







class __snake_case			:







   def __init__(      self		:						str					,						_snake_case		:						TPosition					,						_snake_case		:						TPosition):

        """simple docstring"""


        UpperCAmelCase_          =							Node(start[1]					,						start[0]					,						goal[1]					,						goal[0]					,						0					,						_snake_case)
        UpperCAmelCase_          =							Node(goal[1]					,						goal[0]					,						goal[1]					,						goal[0]					,						99999					,						_snake_case)

        UpperCAmelCase_          =							[self.start]
        UpperCAmelCase_          =							[]

        UpperCAmelCase_          =							False







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        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:
                  return self.retrace_path(_snake_case)

             self.closed_nodes.append(_snake_case)
             UpperCAmelCase_          =							self.get_successors(_snake_case)

             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(_snake_case)
                  else:
                       # retrieve the best current path
                       UpperCAmelCase_          =							self.open_nodes.pop(self.open_nodes.index(_snake_case))

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

        return [self.start.pos]







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Node):

        """simple docstring"""


        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(_snake_case) - 1):
                  continue

             if grid[pos_y][pos_x] != 0:
                  continue

             successors.append(
                 Node(
                     _snake_case					,						_snake_case					,						self.target.pos_y					,						self.target.pos_x					,						parent.g_cost + 1					,						_snake_case					,						))
        return successors







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Node | None):

        """simple docstring"""


        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







class __snake_case			:







   def __init__(      self		:						Any					,						_snake_case		:						TPosition					,						_snake_case		:						TPosition):

        """simple docstring"""


        UpperCAmelCase_          =							AStar(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							AStar(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							False







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
             self.fwd_astar.open_nodes.sort()
             self.bwd_astar.open_nodes.sort()
             UpperCAmelCase_          =							self.fwd_astar.open_nodes.pop(0)
             UpperCAmelCase_          =							self.bwd_astar.open_nodes.pop(0)

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

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

             UpperCAmelCase_          =							current_bwd_node
             UpperCAmelCase_          =							current_fwd_node

             UpperCAmelCase_          =							{
                 self.fwd_astar: self.fwd_astar.get_successors(_snake_case),
                 self.bwd_astar: self.bwd_astar.get_successors(_snake_case),
             }

             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(_snake_case)
                       else:
                            # retrieve the best current path
                            UpperCAmelCase_          =							astar.open_nodes.pop(
                                astar.open_nodes.index(_snake_case))

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

        return [self.fwd_astar.start.pos]







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Node					,						_snake_case		:						Node):

        """simple docstring"""


        UpperCAmelCase_          =							self.fwd_astar.retrace_path(_snake_case)
        UpperCAmelCase_          =							self.bwd_astar.retrace_path(_snake_case)
        bwd_path.pop()
        bwd_path.reverse()
        UpperCAmelCase_          =							fwd_path + bwd_path
        return path


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

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

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






