






"""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       __A		(   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  :      str					)			->							int:
											__magic_name__:	str							=       []
											__magic_name__:	Any							=       []

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

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

																	__magic_name__:	List[str]							=       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					)

											__magic_name__:	Any							=       resnets
											__magic_name__:	str							=       attentions

											if self.add_downsample:
																	__magic_name__:	List[str]							=       FlaxDownsampleaD(self.out_channels     ,		dtype=self.dtype					)






					def __call__(			self  :      Any     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Dict     ,		__snake_case  :      List[str]=True					)			->							str:
											__magic_name__:	str							=       ()

											for resnet, attn in zip(self.resnets     ,		self.attentions					):
																	__magic_name__:	Optional[Any]							=       resnet(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)
																	__magic_name__:	Union[str, Any]							=       attn(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)
																	output_states += (hidden_states,)

											if self.add_downsample:
																	__magic_name__:	Any							=       self.downsamplers_a(__snake_case					)
																	output_states += (hidden_states,)

											return hidden_states, output_states



class       __A		(   nn.Module			):
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				0.0
					UpperCAmelCase__								=				1
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				jnp.floataa

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											__magic_name__:	Optional[Any]							=       []

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

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

											if self.add_downsample:
																	__magic_name__:	str							=       FlaxDownsampleaD(self.out_channels     ,		dtype=self.dtype					)






					def __call__(			self  :      str     ,		__snake_case  :      int     ,		__snake_case  :      List[str]     ,		__snake_case  :      List[Any]=True					)			->							Dict:
											__magic_name__:	Optional[Any]							=       ()

											for resnet in self.resnets:
																	__magic_name__:	Optional[int]							=       resnet(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)
																	output_states += (hidden_states,)

											if self.add_downsample:
																	__magic_name__:	List[str]							=       self.downsamplers_a(__snake_case					)
																	output_states += (hidden_states,)

											return hidden_states, output_states



class       __A		(   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  :      int					)			->							int:
											__magic_name__:	Optional[int]							=       []
											__magic_name__:	Any							=       []

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

																	__magic_name__:	Dict							=       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					)

																	__magic_name__:	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					)

											__magic_name__:	List[str]							=       resnets
											__magic_name__:	List[str]							=       attentions

											if self.add_upsample:
																	__magic_name__:	str							=       FlaxUpsampleaD(self.out_channels     ,		dtype=self.dtype					)






					def __call__(			self  :      Tuple     ,		__snake_case  :      int     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      List[str]     ,		__snake_case  :      Dict=True					)			->							List[Any]:
											for resnet, attn in zip(self.resnets     ,		self.attentions					):
																	# pop res hidden states
																	__magic_name__:	Any							=       res_hidden_states_tuple[-1]
																	__magic_name__:	Optional[int]							=       res_hidden_states_tuple[:-1]
																	__magic_name__:	Optional[Any]							=       jnp.concatenate((hidden_states, res_hidden_states)     ,		axis=-1					)

																	__magic_name__:	Optional[Any]							=       resnet(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)
																	__magic_name__:	str							=       attn(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)

											if self.add_upsample:
																	__magic_name__:	Optional[Any]							=       self.upsamplers_a(__snake_case					)

											return hidden_states



class       __A		(   nn.Module			):
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				0.0
					UpperCAmelCase__								=				1
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				jnp.floataa

					def   lowerCamelCase__   (			self  :      int					)			->							Union[str, Any]:
											__magic_name__:	Any							=       []

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

																	__magic_name__:	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					)

											__magic_name__:	Any							=       resnets

											if self.add_upsample:
																	__magic_name__:	List[str]							=       FlaxUpsampleaD(self.out_channels     ,		dtype=self.dtype					)






					def __call__(			self  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      Tuple     ,		__snake_case  :      str     ,		__snake_case  :      Optional[int]=True					)			->							int:
											for resnet in self.resnets:
																	# pop res hidden states
																	__magic_name__:	List[Any]							=       res_hidden_states_tuple[-1]
																	__magic_name__:	Dict							=       res_hidden_states_tuple[:-1]
																	__magic_name__:	Dict							=       jnp.concatenate((hidden_states, res_hidden_states)     ,		axis=-1					)

																	__magic_name__:	Optional[int]							=       resnet(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)

											if self.add_upsample:
																	__magic_name__:	Optional[Any]							=       self.upsamplers_a(__snake_case					)

											return hidden_states



class       __A		(   nn.Module			):
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				0.0
					UpperCAmelCase__								=				1
					UpperCAmelCase__								=				1
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				jnp.floataa

					def   lowerCamelCase__   (			self  :      Dict					)			->							Dict:
											# there is always at least one resnet
											__magic_name__:	Tuple							=       [
											    FlaxResnetBlockaD(
											        in_channels=self.in_channels     ,		out_channels=self.in_channels     ,		dropout_prob=self.dropout     ,		dtype=self.dtype     ,		)
											]

											__magic_name__:	str							=       []

											for _ in range(self.num_layers					):
																	__magic_name__:	List[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					)

																	__magic_name__:	Tuple							=       FlaxResnetBlockaD(
																	    in_channels=self.in_channels     ,		out_channels=self.in_channels     ,		dropout_prob=self.dropout     ,		dtype=self.dtype     ,		)
																	resnets.append(__snake_case					)

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






					def __call__(			self  :      str     ,		__snake_case  :      Tuple     ,		__snake_case  :      Tuple     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Optional[Any]=True					)			->							int:
											__magic_name__:	Optional[Any]							=       self.resnets[0](__snake_case     ,		__snake_case					)
											for attn, resnet in zip(self.attentions     ,		self.resnets[1:]					):
																	__magic_name__:	Optional[Any]							=       attn(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)
																	__magic_name__:	str							=       resnet(__snake_case     ,		__snake_case     ,		deterministic=__snake_case					)

											return hidden_states









'''simple docstring'''



from __future__ import annotations

import queue






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						data
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None




def   lowercase__(			):

      """simple docstring"""






      print('\n********Press N to stop entering at any point of time********\n'    )
      SCREAMING_SNAKE_CASE   :      str    					=						input('Enter the value of the root node: '    ).strip().lower()
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      SCREAMING_SNAKE_CASE   :      Dict    					=						TreeNode(int(__UpperCamelCase    )    )
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						q.get()
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"Enter the left node of {node_found.data}: "
            SCREAMING_SNAKE_CASE   :      Any    					=						input(__UpperCamelCase    ).strip().lower() or 'n'
            if check == "n":
                  return tree_node
            SCREAMING_SNAKE_CASE   :      str    					=						TreeNode(int(__UpperCamelCase    )    )
            SCREAMING_SNAKE_CASE   :      Any    					=						left_node
            q.put(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						f"Enter the right node of {node_found.data}: "
            SCREAMING_SNAKE_CASE   :      Dict    					=						input(__UpperCamelCase    ).strip().lower() or 'n'
            if check == "n":
                  return tree_node
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						TreeNode(int(__UpperCamelCase    )    )
            SCREAMING_SNAKE_CASE   :      Any    					=						right_node
            q.put(__UpperCamelCase    )
      raise




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      print(node.data      ,end=','    )
      pre_order(node.left    )
      pre_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      in_order(node.left    )
      print(node.data      ,end=','    )
      in_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      post_order(node.left    )
      post_order(node.right    )
      print(node.data      ,end=','    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						q.get()
            print(node_dequeued.data      ,end=','    )
            if node_dequeued.left:
                  q.put(node_dequeued.left    )
            if node_dequeued.right:
                  q.put(node_dequeued.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
            while not q.empty():
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						q.get()
                  print(node_dequeued.data      ,end=','    )
                  if node_dequeued.left:
                        list_.append(node_dequeued.left    )
                  if node_dequeued.right:
                        list_.append(node_dequeued.right    )
            print()
            for node in list_:
                  q.put(__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						node
      while n or stack:
            while n:  # start from root node, find its left child
                  print(n.data      ,end=','    )
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Any    					=						n.left
            # end of while means current node doesn't have left child
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						stack.pop()
            # start to traverse its right child
            SCREAMING_SNAKE_CASE   :      Any    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						node
      while n or stack:
            while n:
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						n.left
            SCREAMING_SNAKE_CASE   :      Tuple    					=						stack.pop()
            print(n.data      ,end=','    )
            SCREAMING_SNAKE_CASE   :      str    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						[], []
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						node
      stacka.append(__UpperCamelCase    )
      while stacka:  # to find the reversed order of post order, store it in stack2
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						stacka.pop()
            if n.left:
                  stacka.append(n.left    )
            if n.right:
                  stacka.append(n.right    )
            stacka.append(__UpperCamelCase    )
      while stacka:  # pop up from stack2 will be the post order
            print(stacka.pop().data      ,end=','    )




def   lowercase__(			__UpperCamelCase:					str = ""      ,__UpperCamelCase:					Dict=50      ,__UpperCamelCase:					Optional[int]="*"    ):

      """simple docstring"""






      if not s:
            return "\n" + width * char
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						divmod(width - len(__UpperCamelCase    ) - 2      ,2    )
      return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(prompt("Binary Tree Traversals"))

      UpperCamelCase_    	=				build_tree()
      print(prompt("Pre Order Traversal"))
      pre_order(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal"))
      in_order(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal"))
      post_order(node)
      print(prompt() + "\n")

      print(prompt("Level Order Traversal"))
      level_order(node)
      print(prompt() + "\n")

      print(prompt("Actual Level Order Traversal"))
      level_order_actual(node)
      print("*" * 5_0 + "\n")

      print(prompt("Pre Order Traversal - Iteration Version"))
      pre_order_iter(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal - Iteration Version"))
      in_order_iter(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal - Iteration Version"))
      post_order_iter(node)
      print(prompt())


