


"""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 	lowercase__				(					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 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[Any]   =		[]
      _lowerCamelCase	:					List[Any]   =		[]

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

         _lowerCamelCase	:					Optional[Any]   =		FlaxResnetBlockaD(
             in_channels=SCREAMING_SNAKE_CASE      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					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(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		resnets
      _lowerCamelCase	:					Tuple   =		attentions

      if self.add_downsample:
         _lowerCamelCase	:					int   =		FlaxDownsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  Optional[int]:
      _lowerCamelCase	:					Tuple   =		()

      for resnet, attn in zip(self.resnets      ,   self.attentions):
         _lowerCamelCase	:					List[str]   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      if self.add_downsample:
         _lowerCamelCase	:					int   =		self.downsamplers_a(SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

      return hidden_states, output_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[int]   =		[]

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

         _lowerCamelCase	:					Optional[Any]   =		FlaxResnetBlockaD(
             in_channels=SCREAMING_SNAKE_CASE      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		resnets

      if self.add_downsample:
         _lowerCamelCase	:					Optional[int]   =		FlaxDownsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  Tuple:
      _lowerCamelCase	:					Optional[int]   =		()

      for resnet in self.resnets:
         _lowerCamelCase	:					Dict   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         output_states += (hidden_states,)

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

      return hidden_states, output_states







class 	lowercase__				(					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 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		[]
      _lowerCamelCase	:					List[str]   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Union[str, Any]   =		self.in_channels if (i == self.num_layers - 1) else self.out_channels
         _lowerCamelCase	:					Optional[Any]   =		self.prev_output_channel if i == 0 else self.out_channels

         _lowerCamelCase	:					Optional[int]   =		FlaxResnetBlockaD(
             in_channels=resnet_in_channels + res_skip_channels      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Tuple   =		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(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		resnets
      _lowerCamelCase	:					Optional[Any]   =		attentions

      if self.add_upsample:
         _lowerCamelCase	:					List[Any]   =		FlaxUpsampleaD(self.out_channels      ,   dtype=self.dtype)





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

         _lowerCamelCase	:					str   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      if self.add_upsample:
         _lowerCamelCase	:					Any   =		self.upsamplers_a(SCREAMING_SNAKE_CASE)

      return hidden_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					True
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					str   =		[]

      for i in range(self.num_layers):
         _lowerCamelCase	:					Union[str, Any]   =		self.in_channels if (i == self.num_layers - 1) else self.out_channels
         _lowerCamelCase	:					Optional[Any]   =		self.prev_output_channel if i == 0 else self.out_channels

         _lowerCamelCase	:					Dict   =		FlaxResnetBlockaD(
             in_channels=resnet_in_channels + res_skip_channels      ,   out_channels=self.out_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		resnets

      if self.add_upsample:
         _lowerCamelCase	:					Dict   =		FlaxUpsampleaD(self.out_channels      ,   dtype=self.dtype)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  int:
      for resnet in self.resnets:
         # pop res hidden states
         _lowerCamelCase	:					Union[str, Any]   =		res_hidden_states_tuple[-1]
         _lowerCamelCase	:					str   =		res_hidden_states_tuple[:-1]
         _lowerCamelCase	:					str   =		jnp.concatenate((hidden_states, res_hidden_states)      ,   axis=-1)

         _lowerCamelCase	:					int   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      if self.add_upsample:
         _lowerCamelCase	:					List[str]   =		self.upsamplers_a(SCREAMING_SNAKE_CASE)

      return hidden_states







class 	lowercase__				(					nn.Module				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					jnp.floataa





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      # there is always at least one resnet
      _lowerCamelCase	:					Optional[int]   =		[
          FlaxResnetBlockaD(
              in_channels=self.in_channels      ,   out_channels=self.in_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
      ]

      _lowerCamelCase	:					str   =		[]

      for _ in range(self.num_layers):
         _lowerCamelCase	:					Dict   =		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(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					List[Any]   =		FlaxResnetBlockaD(
             in_channels=self.in_channels      ,   out_channels=self.in_channels      ,   dropout_prob=self.dropout      ,   dtype=self.dtype      ,   )
         resnets.append(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		resnets
      _lowerCamelCase	:					List[str]   =		attentions





   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		self.resnets[0](SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      for attn, resnet in zip(self.attentions      ,   self.resnets[1:]):
         _lowerCamelCase	:					Dict   =		attn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		resnet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   deterministic=SCREAMING_SNAKE_CASE)

      return hidden_states





"""simple docstring"""
from __future__ import annotations

import queue




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		data
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					Any   =		None







def 				_snake_case      (   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Any   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Optional[Any]   =		[]
      while not q.empty():
         _lowerCamelCase	:					Dict   =		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(__snake_case   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					int   =		node
   while n or stack:
      while n:
         stack.append(__snake_case   )
         _lowerCamelCase	:					Any   =		n.left
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      print(n.data						, end=""","""   )
      _lowerCamelCase	:					List[Any]   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		[], []
   _lowerCamelCase	:					Optional[Any]   =		node
   stacka.append(__snake_case   )
   while stacka:  # to find the reversed order of post order, store it in stack2
      _lowerCamelCase	:					Union[str, Any]   =		stacka.pop()
      if n.left:
         stacka.append(n.left   )
      if n.right:
         stacka.append(n.right   )
      stacka.append(__snake_case   )
   while stacka:  # pop up from stack2 will be the post order
      print(stacka.pop().data						, end=""","""   )

def 				_snake_case      (   __snake_case							:      str = ""						, __snake_case							:      Any=50						, __snake_case							:      List[str]="*"   ):




   """simple docstring"""







   if not s:
      return "\n" + width * char
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		divmod(width - len(__snake_case   ) - 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("""*""" * 50 + """\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())

