





"""simple docstring"""




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""image_processor""", """tokenizer"""]
  a_      =			"""Pix2StructImageProcessor"""
  a_      =			("""T5Tokenizer""", """T5TokenizerFast""")






  def __init__(    self			:		str ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		str					)							->						List[Any]:
     __lowerCAmelCase							     =     False
     super().__init__(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def __call__(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Union[bool, str, PaddingStrategy] = False ,  lowerCAmelCase_			:		Union[bool, str, TruncationStrategy] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = 2_0_4_8 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[bool] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  **lowerCAmelCase_			:		Dict ,  )							->						BatchEncoding:
     if images is None and text is None:
        raise ValueError('You have to specify either images or text.'					)

     # Get only text
     if images is None and not self.image_processor.is_vqa:
        __lowerCAmelCase							     =     self.tokenizer
        __lowerCAmelCase							     =     self.tokenizer(
            text=lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  stride=lowerCAmelCase_ ,  pad_to_multiple_of=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  return_overflowing_tokens=lowerCAmelCase_ ,  return_special_tokens_mask=lowerCAmelCase_ ,  return_offsets_mapping=lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_ ,  return_length=lowerCAmelCase_ ,  verbose=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
        return text_encoding

     if not self.image_processor.is_vqa:
        # add pixel_values
        __lowerCAmelCase							     =     self.image_processor(
            lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  max_patches=lowerCAmelCase_ ,  **lowerCAmelCase_					)
     else:
        # add pixel_values and bbox
        __lowerCAmelCase							     =     self.image_processor(
            lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  max_patches=lowerCAmelCase_ ,  header_text=lowerCAmelCase_ ,  **lowerCAmelCase_					)

     if text is not None and not self.image_processor.is_vqa:
        __lowerCAmelCase							     =     self.tokenizer(
            text=lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  stride=lowerCAmelCase_ ,  pad_to_multiple_of=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  return_overflowing_tokens=lowerCAmelCase_ ,  return_special_tokens_mask=lowerCAmelCase_ ,  return_offsets_mapping=lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_ ,  return_length=lowerCAmelCase_ ,  verbose=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )

        if "attention_mask" in text_encoding:
           __lowerCAmelCase							     =     text_encoding.pop('attention_mask'					)
        if "input_ids" in text_encoding:
           __lowerCAmelCase							     =     text_encoding.pop('input_ids'					)
     else:
        __lowerCAmelCase							     =     None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCAmelCase_					)

     return encoding_image_processor






  def     lowercase		(    self			:		List[str] ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		str					)							->						List[Any]:
     return self.tokenizer.batch_decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int] ,  *lowerCAmelCase_			:		Dict ,  **lowerCAmelCase_			:		str					)							->						Optional[Any]:
     return self.tokenizer.decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		List[Any]					)							->						str:
     __lowerCAmelCase							     =     self.tokenizer.model_input_names
     __lowerCAmelCase							     =     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)








from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_snake_case  :   Dict						   =  0

_snake_case  :   Dict						   =  [
    [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  :   List[str]						   =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_snake_case  :   Tuple						   =  tuple[int, int]



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Node | None ,  )							->						None:
     __lowerCAmelCase							     =     pos_x
     __lowerCAmelCase							     =     pos_y
     __lowerCAmelCase							     =     (pos_y, pos_x)
     __lowerCAmelCase							     =     goal_x
     __lowerCAmelCase							     =     goal_y
     __lowerCAmelCase							     =     g_cost
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     self.calculate_heuristic()
     __lowerCAmelCase							     =     self.g_cost + self.h_cost






  def     lowercase		(    self			:		Any					)							->						float:
     __lowerCAmelCase							     =     self.pos_x - self.goal_x
     __lowerCAmelCase							     =     self.pos_y - self.goal_y
     if HEURISTIC == 1:
        return abs(lowerCAmelCase_					) + abs(lowerCAmelCase_					)
     else:
        return sqrt(dy**2 + dx**2					)






  def __lt__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Node					)							->						bool:
     return self.f_cost < other.f_cost





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						Tuple:
     __lowerCAmelCase							     =     Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_9_9_9_9 ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     [self.start]
     __lowerCAmelCase							     =     []

     __lowerCAmelCase							     =     False






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

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

        self.closed_nodes.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     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
              __lowerCAmelCase							     =     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_					)

     return [self.start.pos]






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Node					)							->						list[Node]:
     __lowerCAmelCase							     =     []
     for action in delta:
        __lowerCAmelCase							     =     parent.pos_x + action[1]
        __lowerCAmelCase							     =     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     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Node | None					)							->						list[TPosition]:
     __lowerCAmelCase							     =     node
     __lowerCAmelCase							     =     []
     while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)					)
        __lowerCAmelCase							     =     current_node.parent
     path.reverse()
     return path





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		int ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						None:
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     False






  def     lowercase		(    self			:		Dict					)							->						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()
        __lowerCAmelCase							     =     self.fwd_astar.open_nodes.pop(0					)
        __lowerCAmelCase							     =     self.bwd_astar.open_nodes.pop(0					)

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

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

        __lowerCAmelCase							     =     current_bwd_node
        __lowerCAmelCase							     =     current_fwd_node

        __lowerCAmelCase							     =     {
            self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase_					),
            self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase_					),
        }

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

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

     return [self.fwd_astar.start.pos]






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Node ,  lowerCAmelCase_			:		Node					)							->						list[TPosition]:
     __lowerCAmelCase							     =     self.fwd_astar.retrace_path(lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.bwd_astar.retrace_path(lowerCAmelCase_					)
     bwd_path.pop()
     bwd_path.reverse()
     __lowerCAmelCase							     =     fwd_path + bwd_path
     return path


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

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

     _snake_case  :   Any						   =  time.time()
     _snake_case  :   Dict						   =  BidirectionalAStar(init, goal)
     _snake_case  :   Optional[int]						   =  time.time() - bd_start_time
     print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")

