

'''simple docstring'''



from __future__ import annotations

UpperCAmelCase					:			Optional[Any]       =	list[tuple[int, int]]

UpperCAmelCase					:			Optional[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],
]

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







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			float   ,				__SCREAMING_SNAKE_CASE   :			Node | None   ,				)    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = pos_x
      __SCREAMING_SNAKE_CASE      = pos_y
      __SCREAMING_SNAKE_CASE      = (pos_y, pos_x)
      __SCREAMING_SNAKE_CASE      = goal_x
      __SCREAMING_SNAKE_CASE      = goal_y
      __SCREAMING_SNAKE_CASE      = g_cost
      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = self.calculate_heuristic()
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					float:


      """simple docstring"""




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


 def __lt__(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]     )    ->					bool:


      """simple docstring"""




      return self.f_cost < other.f_cost







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			tuple[int, int]   ,				__SCREAMING_SNAKE_CASE   :			tuple[int, int]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Node(start[1]   ,				start[0]   ,				goal[1]   ,				goal[0]   ,				0   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = Node(goal[1]   ,				goal[0]   ,				goal[1]   ,				goal[0]   ,				99_999   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = [self.start]
      __SCREAMING_SNAKE_CASE      = []

      __SCREAMING_SNAKE_CASE      = False
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Path | None:


      """simple docstring"""




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

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

           self.closed_nodes.append(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = self.get_successors(__SCREAMING_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(__SCREAMING_SNAKE_CASE     )
                else:
                     # retrieve the best current path
                     __SCREAMING_SNAKE_CASE      = self.open_nodes.pop(self.open_nodes.index(__SCREAMING_SNAKE_CASE     )     )

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

      if not self.reached:
           return [self.start.pos]
      return None
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Node     )    ->					list[Node]:


      """simple docstring"""




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

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

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

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


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Node | None     )    ->					Path:


      """simple docstring"""




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


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

  print('------')

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

    for elem in grid:
      print(elem)









'''simple docstring'''



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__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """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."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					BatchEncoding:


      """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
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = 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=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

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

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




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

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

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




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






