


'''simple docstring'''






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=0.0
       UpperCAmelCase										=1
       UpperCAmelCase										=1
       UpperCAmelCase										=True
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=jnp.floataa






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =[]
          _UpperCAmelCase       :       int							   =[]

          for i in range(self.num_layers):
             _UpperCAmelCase       :       Optional[Any]							   =self.in_channels if i == 0 else self.out_channels

             _UpperCAmelCase       :       Optional[int]							   =FlaxResnetBlockaD(
                 in_channels=snake_case  ,							out_channels=self.out_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
             resnets.append(snake_case)

             _UpperCAmelCase       :       Union[str, Any]							   =FlaxTransformeraDModel(
                 in_channels=self.out_channels  ,							n_heads=self.num_attention_heads  ,							d_head=self.out_channels // self.num_attention_heads  ,							depth=1  ,							use_linear_projection=self.use_linear_projection  ,							only_cross_attention=self.only_cross_attention  ,							use_memory_efficient_attention=self.use_memory_efficient_attention  ,							dtype=self.dtype  ,							)
             attentions.append(snake_case)

          _UpperCAmelCase       :       str							   =resnets
          _UpperCAmelCase       :       Optional[int]							   =attentions

          if self.add_downsample:
             _UpperCAmelCase       :       Any							   =FlaxDownsampleaD(self.out_channels  ,							dtype=self.dtype)






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=True)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =()

          for resnet, attn in zip(self.resnets  ,							self.attentions):
             _UpperCAmelCase       :       int							   =resnet(snake_case  ,							snake_case  ,							deterministic=snake_case)
             _UpperCAmelCase       :       Tuple							   =attn(snake_case  ,							snake_case  ,							deterministic=snake_case)
             output_states += (hidden_states,)

          if self.add_downsample:
             _UpperCAmelCase       :       Optional[Any]							   =self.downsamplers_a(snake_case)
             output_states += (hidden_states,)

          return hidden_states, output_states






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=0.0
       UpperCAmelCase										=1
       UpperCAmelCase										=True
       UpperCAmelCase										=jnp.floataa






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =[]

          for i in range(self.num_layers):
             _UpperCAmelCase       :       Any							   =self.in_channels if i == 0 else self.out_channels

             _UpperCAmelCase       :       Tuple							   =FlaxResnetBlockaD(
                 in_channels=snake_case  ,							out_channels=self.out_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
             resnets.append(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =resnets

          if self.add_downsample:
             _UpperCAmelCase       :       Any							   =FlaxDownsampleaD(self.out_channels  ,							dtype=self.dtype)






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case=True)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =()

          for resnet in self.resnets:
             _UpperCAmelCase       :       str							   =resnet(snake_case  ,							snake_case  ,							deterministic=snake_case)
             output_states += (hidden_states,)

          if self.add_downsample:
             _UpperCAmelCase       :       List[Any]							   =self.downsamplers_a(snake_case)
             output_states += (hidden_states,)

          return hidden_states, output_states






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=0.0
       UpperCAmelCase										=1
       UpperCAmelCase										=1
       UpperCAmelCase										=True
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=jnp.floataa






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =[]
          _UpperCAmelCase       :       Optional[Any]							   =[]

          for i in range(self.num_layers):
             _UpperCAmelCase       :       int							   =self.in_channels if (i == self.num_layers - 1) else self.out_channels
             _UpperCAmelCase       :       Dict							   =self.prev_output_channel if i == 0 else self.out_channels

             _UpperCAmelCase       :       List[Any]							   =FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels  ,							out_channels=self.out_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
             resnets.append(snake_case)

             _UpperCAmelCase       :       Optional[int]							   =FlaxTransformeraDModel(
                 in_channels=self.out_channels  ,							n_heads=self.num_attention_heads  ,							d_head=self.out_channels // self.num_attention_heads  ,							depth=1  ,							use_linear_projection=self.use_linear_projection  ,							only_cross_attention=self.only_cross_attention  ,							use_memory_efficient_attention=self.use_memory_efficient_attention  ,							dtype=self.dtype  ,							)
             attentions.append(snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =resnets
          _UpperCAmelCase       :       Any							   =attentions

          if self.add_upsample:
             _UpperCAmelCase       :       Any							   =FlaxUpsampleaD(self.out_channels  ,							dtype=self.dtype)






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=True)						->  Optional[int]:




          '''simple docstring'''
          for resnet, attn in zip(self.resnets  ,							self.attentions):
             # pop res hidden states
             _UpperCAmelCase       :       Union[str, Any]							   =res_hidden_states_tuple[-1]
             _UpperCAmelCase       :       Any							   =res_hidden_states_tuple[:-1]
             _UpperCAmelCase       :       str							   =jnp.concatenate((hidden_states, res_hidden_states)  ,							axis=-1)

             _UpperCAmelCase       :       Union[str, Any]							   =resnet(snake_case  ,							snake_case  ,							deterministic=snake_case)
             _UpperCAmelCase       :       Dict							   =attn(snake_case  ,							snake_case  ,							deterministic=snake_case)

          if self.add_upsample:
             _UpperCAmelCase       :       List[Any]							   =self.upsamplers_a(snake_case)

          return hidden_states






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=0.0
       UpperCAmelCase										=1
       UpperCAmelCase										=True
       UpperCAmelCase										=jnp.floataa






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =[]

          for i in range(self.num_layers):
             _UpperCAmelCase       :       List[Any]							   =self.in_channels if (i == self.num_layers - 1) else self.out_channels
             _UpperCAmelCase       :       Tuple							   =self.prev_output_channel if i == 0 else self.out_channels

             _UpperCAmelCase       :       Any							   =FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels  ,							out_channels=self.out_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
             resnets.append(snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =resnets

          if self.add_upsample:
             _UpperCAmelCase       :       int							   =FlaxUpsampleaD(self.out_channels  ,							dtype=self.dtype)






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=True)						->  Tuple:




          '''simple docstring'''
          for resnet in self.resnets:
             # pop res hidden states
             _UpperCAmelCase       :       Dict							   =res_hidden_states_tuple[-1]
             _UpperCAmelCase       :       Any							   =res_hidden_states_tuple[:-1]
             _UpperCAmelCase       :       str							   =jnp.concatenate((hidden_states, res_hidden_states)  ,							axis=-1)

             _UpperCAmelCase       :       Optional[int]							   =resnet(snake_case  ,							snake_case  ,							deterministic=snake_case)

          if self.add_upsample:
             _UpperCAmelCase       :       Dict							   =self.upsamplers_a(snake_case)

          return hidden_states






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=0.0
       UpperCAmelCase										=1
       UpperCAmelCase										=1
       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=jnp.floataa






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          # there is always at least one resnet
          _UpperCAmelCase       :       str							   =[
              FlaxResnetBlockaD(
                  in_channels=self.in_channels  ,							out_channels=self.in_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
          ]

          _UpperCAmelCase       :       Tuple							   =[]

          for _ in range(self.num_layers):
             _UpperCAmelCase       :       Any							   =FlaxTransformeraDModel(
                 in_channels=self.in_channels  ,							n_heads=self.num_attention_heads  ,							d_head=self.in_channels // self.num_attention_heads  ,							depth=1  ,							use_linear_projection=self.use_linear_projection  ,							use_memory_efficient_attention=self.use_memory_efficient_attention  ,							dtype=self.dtype  ,							)
             attentions.append(snake_case)

             _UpperCAmelCase       :       Optional[int]							   =FlaxResnetBlockaD(
                 in_channels=self.in_channels  ,							out_channels=self.in_channels  ,							dropout_prob=self.dropout  ,							dtype=self.dtype  ,							)
             resnets.append(snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =resnets
          _UpperCAmelCase       :       int							   =attentions






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=True)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =self.resnets[0](snake_case  ,							snake_case)
          for attn, resnet in zip(self.attentions  ,							self.resnets[1:]):
             _UpperCAmelCase       :       Tuple							   =attn(snake_case  ,							snake_case  ,							deterministic=snake_case)
             _UpperCAmelCase       :       List[str]							   =resnet(snake_case  ,							snake_case  ,							deterministic=snake_case)

          return hidden_states





'''simple docstring'''






import numpy as np






class  __magic_name__							:






       def __init__(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




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






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

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

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


if __name__ == "__main__":
 lowercase							=Gridworld()
 # Start position and goal
 lowercase							=Cell()
 lowercase							=(0, 0)
 lowercase							=Cell()
 lowercase							=(4, 4)
 print(F"""path from {start.position} to {goal.position}""")
 lowercase							=astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  lowercase							=1
 print(world.w)

