
import numpy as np



class    _snake_case :


 def __init__(       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   (0, 0)
   lowercase__	: Optional[int]     		=   None
   lowercase__	: Optional[int]     		=   0
   lowercase__	: Optional[Any]     		=   0
   lowercase__	: Tuple     		=   0


 def __eq__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.position == cell.position



 def   lowercase__  (       self):


   '''simple docstring'''




   print(self.position)



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=(5, 5)):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   np.zeros(SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   world_size[0]
   lowercase__	: Tuple     		=   world_size[1]


 def   lowercase__  (       self):


   '''simple docstring'''




   print(self.w)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   [
       (-1, -1),
       (-1, 0),
       (-1, 1),
       (0, -1),
       (0, 1),
       (1, -1),
       (1, 0),
       (1, 1),
   ]
   lowercase__	: int     		=   cell.position[0]
   lowercase__	: Optional[Any]     		=   cell.position[1]
   lowercase__	: Tuple     		=   []
   for n in neughbour_cord:
     lowercase__	: List[Any]     		=   current_x + n[0]
     lowercase__	: List[str]     		=   current_y + n[1]
     if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
       lowercase__	: Tuple     		=   Cell()
       lowercase__	: str     		=   (x, y)
       lowercase__	: int     		=   cell
       neighbours.append(SCREAMING_SNAKE_CASE_)
   return neighbours

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: List[str]     		=   []
  lowercase__	: int     		=   []
  _open.append(lowercase_ )

  while _open:
    lowercase__	: Tuple     		=   np.argmin([n.f for n in _open] )
    lowercase__	: List[str]     		=   _open[min_f]
    _closed.append(_open.pop(lowercase_ ) )
    if current == goal:
      break
    for n in world.get_neigbours(lowercase_ ):
      for c in _closed:
        if c == n:
          continue
      lowercase__	: Dict     		=   current.g + 1
      lowercase__		,     lowercase__	: Dict     		=   n.position
      lowercase__		,     lowercase__	: int     		=   goal.position
      lowercase__	: Optional[int]     		=   (ya - ya) ** 2 + (xa - xa) ** 2
      lowercase__	: List[str]     		=   n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(lowercase_ )
  lowercase__	: Tuple     		=   []
  while current.parent is not None:
    path.append(current.position )
    lowercase__	: Dict     		=   current.parent
  path.append(current.position )
  return path[::-1]


if __name__ == "__main__":
     lowerCamelCase__     :		Union[str, Any]        = Gridworld()
     # Start position and goal
     lowerCamelCase__     :		List[Any]        = Cell()
     lowerCamelCase__     :		Dict        = (0, 0)
     lowerCamelCase__     :		Dict        = Cell()
     lowerCamelCase__     :		str        = (4, 4)
     print(f'''path from {start.position} to {goal.position}''')
     lowerCamelCase__     :		Tuple        = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCamelCase__     :		Union[str, Any]        = 1
     print(world.w)







"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Dict   	={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=900						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=6						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=8						,			__lowerCAmelCase=6						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=8						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase="sine"						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase=True						,			__lowerCAmelCase=300						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



