

"""simple docstring"""



import numpy as np



class 					a						:



    """simple docstring"""



    def __init__(     self: Any       ):






         """simple docstring"""


         A__										=  (0, 0)
         A__										=  None
         A__										=  0
         A__										=  0
         A__										=  0

    def __eq__(     self: Dict    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         return self.position == cell.position





    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         print(self.position       )



class 					a						:



    """simple docstring"""



    def __init__(     self: List[Any]    ,						UpperCamelCase: Any=(5, 5)       ):






         """simple docstring"""


         A__										=  np.zeros(UpperCamelCase       )
         A__										=  world_size[0]
         A__										=  world_size[1]

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         print(self.w       )





    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         A__										=  [
             (-1, -1),
             (-1, 0),
             (-1, 1),
             (0, -1),
             (0, 1),
             (1, -1),
             (1, 0),
             (1, 1),
         ]
         A__										=  cell.position[0]
         A__										=  cell.position[1]
         A__										=  []
         for n in neughbour_cord:
              A__										=  current_x + n[0]
              A__										=  current_y + n[1]
              if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                   A__										=  Cell()
                   A__										=  (x, y)
                   A__										=  cell
                   neighbours.append(UpperCamelCase       )
         return neighbours





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					List[str]			):
     A__										=  []
     A__										=  []
     _open.append(UpperCAmelCase_			)

     while _open:
          A__										=  np.argmin([n.f for n in _open]			)
          A__										=  _open[min_f]
          _closed.append(_open.pop(UpperCAmelCase_			)			)
          if current == goal:
               break
          for n in world.get_neigbours(UpperCAmelCase_			):
               for c in _closed:
                    if c == n:
                         continue
               A__										=  current.g + 1
               A__			,				A__										=  n.position
               A__			,				A__										=  goal.position
               A__										=  (ya - ya) ** 2 + (xa - xa) ** 2
               A__										=  n.h + n.g

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


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






"""simple docstring"""



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         super().__init__(**UpperCamelCase       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
                  """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,						UpperCamelCase    ,						)

         A__										=  num_channels
         A__										=  num_encoder_blocks
         A__										=  depths
         A__										=  sr_ratios
         A__										=  hidden_sizes
         A__										=  patch_sizes
         A__										=  strides
         A__										=  mlp_ratios
         A__										=  num_attention_heads
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  classifier_dropout_prob
         A__										=  initializer_range
         A__										=  drop_path_rate
         A__										=  layer_norm_eps
         A__										=  decoder_hidden_size
         A__										=  kwargs.get("""reshape_last_stage"""    ,						UpperCamelCase       )
         A__										=  semantic_loss_ignore_index



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]       )

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






