






from __future__ import annotations

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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   UpperCamelCase_									=     [
       [0 for col in range(len(grid[0]))] for row in range(len(__lowercase))
   ]  # the reference grid
   UpperCamelCase_									=     1
   UpperCamelCase_									=     [
       [0 for col in range(len(grid[0]))] for row in range(len(__lowercase))
   ]  # the action grid

   UpperCamelCase_									=     init[0]
   UpperCamelCase_									=     init[1]
   UpperCamelCase_									=     0
   UpperCamelCase_									=     g + heuristic[x][y]  # cost from starting cell to destination cell
   UpperCamelCase_									=     [[f, g, x, y]]

   UpperCamelCase_									=     False  # flag that is set when search is complete
   UpperCamelCase_									=     False  # flag set if we can't find expand

   while not found and not resign:
      if len(__lowercase) == 0:
         raise ValueError('Algorithm is unable to find solution')
      else:  # to choose the least costliest action so as to move closer to the goal
         cell.sort()
         cell.reverse()
         UpperCamelCase_									=     cell.pop()
         UpperCamelCase_									=     next_cell[2]
         UpperCamelCase_									=     next_cell[3]
         UpperCamelCase_									=     next_cell[1]

         if x == goal[0] and y == goal[1]:
            UpperCamelCase_									=     True
         else:
            for i in range(len(__lowercase)):  # to try out different valid actions
               UpperCamelCase_									=     x + DIRECTIONS[i][0]
               UpperCamelCase_									=     y + DIRECTIONS[i][1]
               if xa >= 0 and xa < len(__lowercase) and ya >= 0 and ya < len(grid[0]):
                  if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                     UpperCamelCase_									=     g + cost
                     UpperCamelCase_									=     ga + heuristic[xa][ya]
                     cell.append([fa, ga, xa, ya])
                     UpperCamelCase_									=     1
                     UpperCamelCase_									=     i
   UpperCamelCase_									=     []
   UpperCamelCase_									=     goal[0]
   UpperCamelCase_									=     goal[1]
   invpath.append([x, y])  # we get the reverse path from here
   while x != init[0] or y != init[1]:
      UpperCamelCase_									=     x - DIRECTIONS[action[x][y]][0]
      UpperCamelCase_									=     y - DIRECTIONS[action[x][y]][1]
      UpperCamelCase_									=     xa
      UpperCamelCase_									=     ya
      invpath.append([x, y])

   UpperCamelCase_									=     []
   for i in range(len(__lowercase)):
      path.append(invpath[len(__lowercase) - 1 - i])
   return path, action


if __name__ == "__main__":
 snake_case__      :	Optional[Any]       	=      [
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 1, 0],
     [0, 0, 0, 0, 1, 0],
 ]

 snake_case__      :	str       	=      [0, 0]
 # all coordinates are given in format [y,x]
 snake_case__      :	int       	=      [len(grid) - 1, len(grid[0]) - 1]
 snake_case__      :	Dict       	=      1

 # the cost map which pushes the path closer to the goal
 snake_case__      :	Optional[Any]       	=      [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
 for i in range(len(grid)):
  for j in range(len(grid[0])):
   snake_case__      :	Optional[Any]       	=      abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    snake_case__      :	Tuple       	=      9_9

 snake_case__	,			snake_case__      :	Optional[Any]       	=      search(grid, init, goal, cost, heuristic)

 print("""ACTION MAP""")
 for i in range(len(action)):
  print(action[i])

 for i in range(len(path)):
  print(path[i])













import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

snake_case__      :	List[Any]       	=      {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git_vision_model"""




  def __init__(     self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=3072		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3		,		_UpperCAmelCase=224		,		_UpperCAmelCase=16		,		_UpperCAmelCase="quick_gelu"		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		**_UpperCAmelCase		,		)			-> Optional[Any]:
     super().__init__(**_UpperCAmelCase )

     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     attention_dropout
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     hidden_act




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> "PretrainedConfig":
     cls._set_token_in_kwargs(_UpperCAmelCase )

     UpperCamelCase_		,      UpperCamelCase_									=     cls.get_config_dict(_UpperCAmelCase		,		**_UpperCAmelCase )

     # get the vision config dict if we are loading from GITConfig
     if config_dict.get('model_type' ) == "git":
        UpperCamelCase_									=     config_dict['vision_config']

     if "model_type" in config_dict and hasattr(cls		,		'model_type' ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
            f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

     return cls.from_dict(_UpperCAmelCase		,		**_UpperCAmelCase )




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git"""




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=6		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=101		,		_UpperCAmelCase=102		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Optional[int]:
     super().__init__(bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		**_UpperCAmelCase )

     if vision_config is None:
        UpperCamelCase_									=     {}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.' )

     UpperCamelCase_									=     GitVisionConfig(**_UpperCAmelCase )
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     max_position_embeddings
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     position_embedding_type
     UpperCamelCase_									=     use_cache
     UpperCamelCase_									=     tie_word_embeddings
     UpperCamelCase_									=     num_image_with_embedding

     UpperCamelCase_									=     bos_token_id
     UpperCamelCase_									=     eos_token_id




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     copy.deepcopy(self.__dict__ )
     UpperCamelCase_									=     self.vision_config.to_dict()
     UpperCamelCase_									=     self.__class__.model_type
     return output





