
'''simple docstring'''

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)


@flax.struct.dataclass
class 	a_	(__SCREAMING_SNAKE_CASE     ):
       __lowerCAmelCase     :					List[Any]     =   4_2



@flax_register_to_config
class 	a_	(nn.Module				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE     ):
       __lowerCAmelCase     :					List[Any]     =   3_2
       __lowerCAmelCase     :					Optional[Any]     =   4
       __lowerCAmelCase     :					Optional[int]     =   4
       __lowerCAmelCase     :					List[str]     =   (
           """CrossAttnDownBlock2D""",
           """CrossAttnDownBlock2D""",
           """CrossAttnDownBlock2D""",
           """DownBlock2D""",
       )
       __lowerCAmelCase     :					List[str]     =   ("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
       __lowerCAmelCase     :					Optional[int]     =   False
       __lowerCAmelCase     :					str     =   (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
       __lowerCAmelCase     :					Any     =   2
       __lowerCAmelCase     :					List[str]     =   8
       __lowerCAmelCase     :					Tuple     =   None
       __lowerCAmelCase     :					List[str]     =   1_2_8_0
       __lowerCAmelCase     :					Any     =   0.0
       __lowerCAmelCase     :					List[Any]     =   False
       __lowerCAmelCase     :					Any     =   jnp.floataa
       __lowerCAmelCase     :					Any     =   True
       __lowerCAmelCase     :					Optional[Any]     =   0
       __lowerCAmelCase     :					Dict     =   False




       def 				__UpperCamelCase							(  self , snake_case_	):
         _lowerCAmelCase							:	str 					=       (1, self.in_channels, self.sample_size, self.sample_size)
         _lowerCAmelCase							:	Optional[Any] 					=       jnp.zeros(UpperCamelCase__ , dtype=jnp.floataa	)
         _lowerCAmelCase							:	str 					=       jnp.ones((1,) , dtype=jnp.intaa	)
         _lowerCAmelCase							:	Tuple 					=       jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa	)

         _lowerCAmelCase							:	Union[str, Any] 					=       jax.random.split(UpperCamelCase__	)
         _lowerCAmelCase							:	Optional[Any] 					=       {'''params''': params_rng, '''dropout''': dropout_rng}

         return self.init(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__	)["params"]




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Tuple 					=       self.block_out_channels
         _lowerCAmelCase							:	Any 					=       block_out_channels[0] * 4

         if self.num_attention_heads is not None:
           raise ValueError(
               """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""	)

         # If `num_attention_heads` is not defined (which is the case for most models)
         # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
         # The reason for this behavior is to correct for incorrectly named variables that were introduced
         # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
         # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
         # which is why we correct for the naming here.
         _lowerCAmelCase							:	Tuple 					=       self.num_attention_heads or self.attention_head_dim

         # input
         _lowerCAmelCase							:	int 					=       nn.Conv(
             block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )

         # time
         _lowerCAmelCase							:	Union[str, Any] 					=       FlaxTimesteps(
             block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift	)
         _lowerCAmelCase							:	Union[str, Any] 					=       FlaxTimestepEmbedding(UpperCamelCase__ , dtype=self.dtype	)

         _lowerCAmelCase							:	Union[str, Any] 					=       self.only_cross_attention
         if isinstance(UpperCamelCase__ , UpperCamelCase__	):
           _lowerCAmelCase							:	Dict 					=       (only_cross_attention,) * len(self.down_block_types	)

         if isinstance(UpperCamelCase__ , UpperCamelCase__	):
           _lowerCAmelCase							:	int 					=       (num_attention_heads,) * len(self.down_block_types	)

         # down
         _lowerCAmelCase							:	Optional[Any] 					=       []
         _lowerCAmelCase							:	Optional[Any] 					=       block_out_channels[0]
         for i, down_block_type in enumerate(self.down_block_types	):
           _lowerCAmelCase							:	int 					=       output_channel
           _lowerCAmelCase							:	List[Any] 					=       block_out_channels[i]
           _lowerCAmelCase							:	List[Any] 					=       i == len(UpperCamelCase__	) - 1

           if down_block_type == "CrossAttnDownBlock2D":
             _lowerCAmelCase							:	Tuple 					=       FlaxCrossAttnDownBlockaD(
                 in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
           else:
             _lowerCAmelCase							:	str 					=       FlaxDownBlockaD(
                 in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )

           down_blocks.append(UpperCamelCase__	)
         _lowerCAmelCase							:	Dict 					=       down_blocks

         # mid
         _lowerCAmelCase							:	Tuple 					=       FlaxUNetMidBlockaDCrossAttn(
             in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )

         # up
         _lowerCAmelCase							:	List[str] 					=       []
         _lowerCAmelCase							:	str 					=       list(reversed(UpperCamelCase__	)	)
         _lowerCAmelCase							:	List[str] 					=       list(reversed(UpperCamelCase__	)	)
         _lowerCAmelCase							:	str 					=       list(reversed(UpperCamelCase__	)	)
         _lowerCAmelCase							:	Optional[int] 					=       reversed_block_out_channels[0]
         for i, up_block_type in enumerate(self.up_block_types	):
           _lowerCAmelCase							:	Optional[Any] 					=       output_channel
           _lowerCAmelCase							:	int 					=       reversed_block_out_channels[i]
           _lowerCAmelCase							:	Tuple 					=       reversed_block_out_channels[min(i + 1 , len(UpperCamelCase__	) - 1	)]

           _lowerCAmelCase							:	Union[str, Any] 					=       i == len(UpperCamelCase__	) - 1

           if up_block_type == "CrossAttnUpBlock2D":
             _lowerCAmelCase							:	Optional[int] 					=       FlaxCrossAttnUpBlockaD(
                 in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , prev_output_channel=UpperCamelCase__ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
           else:
             _lowerCAmelCase							:	List[str] 					=       FlaxUpBlockaD(
                 in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , prev_output_channel=UpperCamelCase__ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )

           up_blocks.append(UpperCamelCase__	)
           _lowerCAmelCase							:	Dict 					=       output_channel
         _lowerCAmelCase							:	Optional[int] 					=       up_blocks

         # out
         _lowerCAmelCase							:	Dict 					=       nn.GroupNorm(num_groups=3_2 , epsilon=1E-5	)
         _lowerCAmelCase							:	Optional[int] 					=       nn.Conv(
             self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )







       def __call__(  self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None , snake_case_ = True , snake_case_ = False , ):







         if not isinstance(UpperCamelCase__ , jnp.ndarray	):
           _lowerCAmelCase							:	str 					=       jnp.array([timesteps] , dtype=jnp.intaa	)
         elif isinstance(UpperCamelCase__ , jnp.ndarray	) and len(timesteps.shape	) == 0:
           _lowerCAmelCase							:	Tuple 					=       timesteps.astype(dtype=jnp.floataa	)
           _lowerCAmelCase							:	Tuple 					=       jnp.expand_dims(UpperCamelCase__ , 0	)

         _lowerCAmelCase							:	Union[str, Any] 					=       self.time_proj(UpperCamelCase__	)
         _lowerCAmelCase							:	List[Any] 					=       self.time_embedding(UpperCamelCase__	)

         # 2. pre-process
         _lowerCAmelCase							:	str 					=       jnp.transpose(UpperCamelCase__ , (0, 2, 3, 1)	)
         _lowerCAmelCase							:	List[Any] 					=       self.conv_in(UpperCamelCase__	)

         # 3. down
         _lowerCAmelCase							:	Optional[Any] 					=       (sample,)
         for down_block in self.down_blocks:
           if isinstance(UpperCamelCase__ , UpperCamelCase__	):
             _lowerCAmelCase							:	List[Any] 					=       down_block(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , deterministic=not train	)
           else:
             _lowerCAmelCase							:	Union[str, Any] 					=       down_block(UpperCamelCase__ , UpperCamelCase__ , deterministic=not train	)
           down_block_res_samples += res_samples

         if down_block_additional_residuals is not None:
           _lowerCAmelCase							:	str 					=       ()

           for down_block_res_sample, down_block_additional_residual in zip(
               UpperCamelCase__ , UpperCamelCase__	):
             down_block_res_sample += down_block_additional_residual
             new_down_block_res_samples += (down_block_res_sample,)

           _lowerCAmelCase							:	List[str] 					=       new_down_block_res_samples

         # 4. mid
         _lowerCAmelCase							:	Union[str, Any] 					=       self.mid_block(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , deterministic=not train	)

         if mid_block_additional_residual is not None:
           sample += mid_block_additional_residual

         # 5. up
         for up_block in self.up_blocks:
           _lowerCAmelCase							:	str 					=       down_block_res_samples[-(self.layers_per_block + 1) :]
           _lowerCAmelCase							:	List[Any] 					=       down_block_res_samples[: -(self.layers_per_block + 1)]
           if isinstance(UpperCamelCase__ , UpperCamelCase__	):
             _lowerCAmelCase							:	Union[str, Any] 					=       up_block(
                 UpperCamelCase__ , temb=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , res_hidden_states_tuple=UpperCamelCase__ , deterministic=not train , )
           else:
             _lowerCAmelCase							:	Any 					=       up_block(UpperCamelCase__ , temb=UpperCamelCase__ , res_hidden_states_tuple=UpperCamelCase__ , deterministic=not train	)

        # 6. post-process
         _lowerCAmelCase							:	List[Any] 					=       self.conv_norm_out(UpperCamelCase__	)
         _lowerCAmelCase							:	int 					=       nn.silu(UpperCamelCase__	)
         _lowerCAmelCase							:	Tuple 					=       self.conv_out(UpperCamelCase__	)
         _lowerCAmelCase							:	List[str] 					=       jnp.transpose(UpperCamelCase__ , (0, 3, 1, 2)	)

         if not return_dict:
           return (sample,)

         return FlaxUNetaDConditionOutput(sample=UpperCamelCase__	)




'''simple docstring'''
from __future__ import annotations

import queue






class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			int  ,	UpperCamelCase__				:			Optional[int]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						data
				snake_case  :       Tuple     			=						None
				snake_case  :       int     			=						None







def    _UpperCamelCase						(       )  ->							TreeNode:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       str     			=						[]
				while not q.empty():
						snake_case  :       int     			=						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(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "" ,       SCREAMING_SNAKE_CASE__=50 ,       SCREAMING_SNAKE_CASE__="*"			)  ->							str:


		'''simple docstring'''


		if not s:
				return "\n" + width * char
		snake_case			,snake_case  :       Any     			=						divmod(width - len(SCREAMING_SNAKE_CASE__			) - 2 ,       2			)
		return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

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

						lowercase__						       =      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())


