



"""simple docstring"""







from __future__ import annotations

lowercase__		:				Optional[int]          =	list[tuple[int, int]]

lowercase__		:				List[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],
]

lowercase__		:				Optional[Any]          =	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        lowerCAmelCase_   :				int								= pos_x
        lowerCAmelCase_   :				Optional[int]								= pos_y
        lowerCAmelCase_   :				Dict								= (pos_y, pos_x)
        lowerCAmelCase_   :				Union[str, Any]								= goal_x
        lowerCAmelCase_   :				List[str]								= goal_y
        lowerCAmelCase_   :				Dict								= g_cost
        lowerCAmelCase_   :				Any								= parent
        lowerCAmelCase_   :				List[Any]								= self.calculate_heuristic()



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				str								= abs(self.pos_x - self.goal_x       )
        lowerCAmelCase_   :				int								= abs(self.pos_y - self.goal_y       )
        return dx + dy





      def __lt__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.f_cost < other.f_cost







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= Node(start[1]    ,			start[0]    ,			goal[1]    ,			goal[0]    ,			0    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= Node(goal[1]    ,			goal[0]    ,			goal[1]    ,			goal[0]    ,			9_9_9_9_9    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Union[str, Any]								= [self.start]
        lowerCAmelCase_   :				str								= []

        lowerCAmelCase_   :				List[Any]								= False



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        while self.open_nodes:
          # Open Nodes are sorted using __lt__
          self.open_nodes.sort()
          lowerCAmelCase_   :				str								= self.open_nodes.pop(0       )

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

          self.closed_nodes.append(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= 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
              lowerCAmelCase_   :				Any								= 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      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Union[str, Any]								= []
        for action in delta:
          lowerCAmelCase_   :				Optional[int]								= parent.pos_x + action[1]
          lowerCAmelCase_   :				List[str]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Optional[Any]								= node
        lowerCAmelCase_   :				int								= []
        while current_node is not None:
          path.append((current_node.pos_y, current_node.pos_x)       )
          lowerCAmelCase_   :				Union[str, Any]								= current_node.parent
        path.reverse()
        return path


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

     print("""------""")

     lowercase__		:				Dict          =	GreedyBestFirst(init, goal)
     lowercase__		:				List[str]          =	greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               lowercase__		:				Any          =	2

          for elem in grid:
               print(elem)












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






