



"""simple docstring"""






class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						Any  )->      None:
								lowerCamelCase__	:	dict[str, TrieNode]				={}  # Mapping from char to TrieNode
								lowerCamelCase__	:	Tuple				=False



					def 	snake_case ( self							:						int,						lowerCamelCase							:						list[str]  )->      None:
								for word in words:
											self.insert(lowerCamelCase  )



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						str  )->      None:
								lowerCamelCase__	:	Tuple				=self
								for char in word:
											if char not in curr.nodes:
														lowerCamelCase__	:	str				=TrieNode()
											lowerCamelCase__	:	int				=curr.nodes[char]
								lowerCamelCase__	:	Union[str, Any]				=True



					def 	snake_case ( self							:						str,						lowerCamelCase							:						str  )->      bool:
								lowerCamelCase__	:	Dict				=self
								for char in word:
											if char not in curr.nodes:
														return False
											lowerCamelCase__	:	Tuple				=curr.nodes[char]
								return curr.is_leaf




					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						str  )->      None:

								def _delete(lowerCamelCase							:						TrieNode,						lowerCamelCase							:						str,						lowerCamelCase							:						int  ) -> bool:
											if index == len(lowerCamelCase  ):
														# If word does not exist
														if not curr.is_leaf:
																	return False
														lowerCamelCase__	:	Tuple				=False
														return len(curr.nodes  ) == 0
											lowerCamelCase__	:	Optional[int]				=word[index]
											lowerCamelCase__	:	List[Any]				=curr.nodes.get(lowerCamelCase  )
											# If char not in current trie node
											if not char_node:
														return False
											# Flag to check if node can be deleted
											lowerCamelCase__	:	Optional[int]				=_delete(lowerCamelCase,						lowerCamelCase,						index + 1  )
											if delete_curr:
														del curr.nodes[char]
														return len(curr.nodes  ) == 0
											return delete_curr

								_delete(self,						lowerCamelCase,						0  )






def   snake_case__     (	__lowerCamelCase       :					TrieNode							,	__lowerCamelCase       :					str			):
			"""simple docstring"""


			if node.is_leaf:
						print(_lowerCamelCase							,	end=''' '''			)

			for key, value in node.nodes.items():
						print_words(_lowerCamelCase							,	word + key			)






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				="banana bananas bandana band apple all beast".split()
			lowerCamelCase__	:	Dict				=TrieNode()
			root.insert_many(_lowerCamelCase			)
			# print_words(root, "")
			assert all(root.find(_lowerCamelCase			) for word in words			)
			assert root.find('''banana'''			)
			assert not root.find('''bandanas'''			)
			assert not root.find('''apps'''			)
			assert root.find('''apple'''			)
			assert root.find('''all'''			)
			root.delete('''all'''			)
			assert not root.find('''all'''			)
			root.delete('''banana'''			)
			assert not root.find('''banana'''			)
			assert root.find('''bananas'''			)
			return True






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					bool			):
			"""simple docstring"""
			print(str(_lowerCamelCase			)							,	'''works!''' if passes else '''doesn\'t work :('''			)






def   snake_case__     (	):
			"""simple docstring"""
			assert test_trie()






def   snake_case__     (	):
			"""simple docstring"""
			print_results('''Testing trie functionality'''							,	test_trie()			)


if __name__ == "__main__":
	main()









"""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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)

@flax.struct.dataclass
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							42
					_a         =							42





class  __SCREAMING_SNAKE_CASE			(   nn.Module     ):


					'''simple docstring'''





					_a         =							42
					_a         =							(1_6, 3_2, 9_6, 2_5_6)
					_a         =							jnp.floataa



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Tuple				=nn.Conv(
								    self.block_out_channels[0],						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

								lowerCamelCase__	:	Dict				=[]
								for i in range(len(self.block_out_channels  ) - 1  ):
											lowerCamelCase__	:	Dict				=self.block_out_channels[i]
											lowerCamelCase__	:	Dict				=self.block_out_channels[i + 1]
											lowerCamelCase__	:	List[str]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
											lowerCamelCase__	:	Optional[int]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						strides=(2, 2),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
								lowerCamelCase__	:	Any				=blocks

								lowerCamelCase__	:	Optional[int]				=nn.Conv(
								    self.conditioning_embedding_channels,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						Any,						lowerCamelCase							:						int  )->      List[str]:
								lowerCamelCase__	:	Tuple				=self.conv_in(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=nn.silu(lowerCamelCase  )

								for block in self.blocks:
											lowerCamelCase__	:	str				=block(lowerCamelCase  )
											lowerCamelCase__	:	List[str]				=nn.silu(lowerCamelCase  )

								lowerCamelCase__	:	Any				=self.conv_out(lowerCamelCase  )

								return embedding





@flax_register_to_config
class  __SCREAMING_SNAKE_CASE			(   nn.Module	,     lowerCAmelCase_	,     lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							3_2
					_a         =							4
					_a         =							(
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					_a         =							False
					_a         =							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
					_a         =							2
					_a         =							8
					_a         =							None
					_a         =							1_2_8_0
					_a         =							0.0
					_a         =							False
					_a         =							jnp.floataa
					_a         =							True
					_a         =							0
					_a         =							"rgb"
					_a         =							(1_6, 3_2, 9_6, 2_5_6)



					def 	snake_case ( self							:						str,						lowerCamelCase							:						jax.random.KeyArray  )->      FrozenDict:
								# init input tensors
								lowerCamelCase__	:	int				=(1, self.in_channels, self.sample_size, self.sample_size)
								lowerCamelCase__	:	int				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )
								lowerCamelCase__	:	Union[str, Any]				=jnp.ones((1,),						dtype=jnp.intaa  )
								lowerCamelCase__	:	str				=jnp.zeros((1, 1, self.cross_attention_dim),						dtype=jnp.floataa  )
								lowerCamelCase__	:	Any				=(1, 3, self.sample_size * 8, self.sample_size * 8)
								lowerCamelCase__	:	Optional[Any]				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=jax.random.split(lowerCamelCase  )
								lowerCamelCase__	:	Dict				={'''params''': params_rng, '''dropout''': dropout_rng}

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



					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__	:	Optional[int]				=self.block_out_channels
								lowerCamelCase__	:	Tuple				=block_out_channels[0] * 4

								# 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__	:	List[Any]				=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__	:	str				=FlaxTimesteps(
								    block_out_channels[0],						flip_sin_to_cos=self.flip_sin_to_cos,						freq_shift=self.config.freq_shift  )
								lowerCamelCase__	:	Dict				=FlaxTimestepEmbedding(lowerCamelCase,						dtype=self.dtype  )

								lowerCamelCase__	:	List[Any]				=FlaxControlNetConditioningEmbedding(
								    conditioning_embedding_channels=block_out_channels[0],						block_out_channels=self.conditioning_embedding_out_channels,						)

								lowerCamelCase__	:	Dict				=self.only_cross_attention
								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	int				=(only_cross_attention,) * len(self.down_block_types  )

								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	List[str]				=(num_attention_heads,) * len(self.down_block_types  )

								# down
								lowerCamelCase__	:	Union[str, Any]				=[]
								lowerCamelCase__	:	Dict				=[]

								lowerCamelCase__	:	List[Any]				=block_out_channels[0]

								lowerCamelCase__	:	List[Any]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
								controlnet_down_blocks.append(lowerCamelCase  )

								for i, down_block_type in enumerate(self.down_block_types  ):
											lowerCamelCase__	:	List[Any]				=output_channel
											lowerCamelCase__	:	str				=block_out_channels[i]
											lowerCamelCase__	:	Dict				=i == len(lowerCamelCase  ) - 1

											if down_block_type == "CrossAttnDownBlock2D":
														lowerCamelCase__	:	str				=FlaxCrossAttnDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						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],						dtype=self.dtype,						)
											else:
														lowerCamelCase__	:	List[Any]				=FlaxDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						dropout=self.dropout,						num_layers=self.layers_per_block,						add_downsample=not is_final_block,						dtype=self.dtype,						)

											down_blocks.append(lowerCamelCase  )

											for _ in range(self.layers_per_block  ):
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

											if not is_final_block:
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

								lowerCamelCase__	:	int				=down_blocks
								lowerCamelCase__	:	List[str]				=controlnet_down_blocks

								# mid
								lowerCamelCase__	:	Tuple				=block_out_channels[-1]
								lowerCamelCase__	:	List[Any]				=FlaxUNetMidBlockaDCrossAttn(
								    in_channels=lowerCamelCase,						dropout=self.dropout,						num_attention_heads=num_attention_heads[-1],						use_linear_projection=self.use_linear_projection,						dtype=self.dtype,						)

								lowerCamelCase__	:	List[str]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						int,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str,						lowerCamelCase							:						float = 1.0,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = False,						)->      Union[FlaxControlNetOutput, Tuple]:
								lowerCamelCase__	:	int				=self.controlnet_conditioning_channel_order
								if channel_order == "bgr":
											lowerCamelCase__	:	int				=jnp.flip(lowerCamelCase,						axis=1  )

								# 1. time
								if not isinstance(lowerCamelCase,						jnp.ndarray  ):
											lowerCamelCase__	:	Any				=jnp.array([timesteps],						dtype=jnp.intaa  )
								elif isinstance(lowerCamelCase,						jnp.ndarray  ) and len(timesteps.shape  ) == 0:
											lowerCamelCase__	:	List[str]				=timesteps.astype(dtype=jnp.floataa  )
											lowerCamelCase__	:	int				=jnp.expand_dims(lowerCamelCase,						0  )

								lowerCamelCase__	:	Optional[Any]				=self.time_proj(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=self.time_embedding(lowerCamelCase  )

								# 2. pre-process
								lowerCamelCase__	:	Optional[int]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	Dict				=self.conv_in(lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	int				=self.controlnet_cond_embedding(lowerCamelCase  )
								sample += controlnet_cond

								# 3. down
								lowerCamelCase__	:	Union[str, Any]				=(sample,)
								for down_block in self.down_blocks:
											if isinstance(lowerCamelCase,						lowerCamelCase  ):
														lowerCamelCase__			,     lowerCamelCase__	:	Dict				=down_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											else:
														lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=down_block(lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											down_block_res_samples += res_samples

								# 4. mid
								lowerCamelCase__	:	Optional[int]				=self.mid_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )

								# 5. contronet blocks
								lowerCamelCase__	:	Optional[Any]				=()
								for down_block_res_sample, controlnet_block in zip(lowerCamelCase,						self.controlnet_down_blocks  ):
											lowerCamelCase__	:	Union[str, Any]				=controlnet_block(lowerCamelCase  )
											controlnet_down_block_res_samples += (down_block_res_sample,)

								lowerCamelCase__	:	List[str]				=controlnet_down_block_res_samples

								lowerCamelCase__	:	List[str]				=self.controlnet_mid_block(lowerCamelCase  )

								# 6. scaling
								lowerCamelCase__	:	Union[str, Any]				=[sample * conditioning_scale for sample in down_block_res_samples]
								mid_block_res_sample *= conditioning_scale

								if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

								return FlaxControlNetOutput(
								    down_block_res_samples=lowerCamelCase,						mid_block_res_sample=lowerCamelCase  )




