

from __future__ import annotations

_lowerCamelCase      :					Dict					    =      list[tuple[int, int]]

_lowerCamelCase      :					str					    =      [
    [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      :					Dict					    =      ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   ):







             '''simple docstring'''



             __A    =pos_x
             __A    =pos_y
             __A    =(pos_y, pos_x)
             __A    =goal_x
             __A    =goal_y
             __A    =g_cost
             __A    =parent
             __A    =self.calculate_heuristic()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =abs(self.pos_x - self.goal_x     )
             __A    =abs(self.pos_y - self.goal_y     )
             return dx + dy


       def __lt__(							self ,   lowercase__     ):







             '''simple docstring'''



             return self.f_cost < other.f_cost





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =Node(start[1] ,   start[0] ,   goal[1] ,   goal[0] ,   0 ,   lowercase__     )
             __A    =Node(goal[1] ,   goal[0] ,   goal[1] ,   goal[0] ,   9_9_9_9_9 ,   lowercase__     )

             __A    =[self.start]
             __A    =[]

             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             while self.open_nodes:
                   # Open Nodes are sorted using __lt__
                   self.open_nodes.sort()
                   __A    =self.open_nodes.pop(0     )

                   if current_node.pos == self.target.pos:
                         __A    =True
                         return self.retrace_path(lowercase__     )

                   self.closed_nodes.append(lowercase__     )
                   __A    =self.get_successors(lowercase__     )

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

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

             if not self.reached:
                   return [self.start.pos]
             return None

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for action in delta:
                   __A    =parent.pos_x + action[1]
                   __A    =parent.pos_y + action[0]

                   if not (0 <= pos_x <= len(grid[0]     ) - 1 and 0 <= pos_y <= len(lowercase__     ) - 1):
                         continue

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

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


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =node
             __A    =[]
             while current_node is not None:
                   path.append((current_node.pos_y, current_node.pos_x)     )
                   __A    =current_node.parent
             path.reverse()
             return path


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

   print('''------''')

   _lowerCamelCase      :					Optional[Any]					    =      GreedyBestFirst(init, goal)
   _lowerCamelCase      :					Dict					    =      greedy_bf.search()
   if path:
      for pos_x, pos_y in path:
         _lowerCamelCase      :					Tuple					    =      2

      for elem in grid:
         print(elem)





import warnings
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        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('''You need to specify an `image_processor`.'''     )
             if tokenizer is None:
                   raise ValueError('''You need to specify a `tokenizer`.'''     )

             super().__init__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             if self.image_processor.apply_ocr and (boxes is not None):
                   raise ValueError(
                       '''You cannot provide bounding boxes '''
                       '''if you initialized the image processor with apply_ocr set to True.'''     )

             if self.image_processor.apply_ocr and (word_labels is not None):
                   raise ValueError(
                       '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''     )

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''     )

             # first, apply the image processor
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(lowercase__ ,   lowercase__     ):
                         __A    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   __A    =features['''words''']

             __A    =self.tokenizer(
                 text=text if text is not None else features['''words'''] ,   text_pair=text_pair if text_pair is not None else None ,   boxes=boxes if boxes is not None else features['''boxes'''] ,   word_labels=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

             # add pixel values
             __A    =features.pop('''pixel_values'''     )
             if return_overflowing_tokens is True:
                   __A    =self.get_overflowing_images(lowercase__ ,   encoded_inputs['''overflow_to_sample_mapping''']     )
             __A    =images

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

             if len(lowercase__     ) != len(lowercase__     ):
                   raise ValueError(
                       '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                       f''' {len(lowercase__     )} and {len(lowercase__     )}'''     )

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.batch_decode(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.decode(*lowercase__ ,   **lowercase__     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


