
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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42




@flax_register_to_config
class  lowerCamelCase__							(				nn.Module     ,						__lowercase     ,						__lowercase):




	'''simple docstring'''




	_A								=     3_2
	_A								=     4
	_A								=     4
	_A								=     (
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	_A								=     ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
	_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								=     False







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:jax.random.KeyArray						)		-> FrozenDict:
								# init input tensors
								__UpperCamelCase	:   List[str]			   =					(1, self.in_channels, self.sample_size, self.sample_size)
								__UpperCamelCase	:   str			   =					jnp.zeros(a     ,   dtype=jnp.floataa						)
								__UpperCamelCase	:   str			   =					jnp.ones((1,)     ,   dtype=jnp.intaa						)
								__UpperCamelCase	:   int			   =					jnp.zeros((1, 1, self.cross_attention_dim)     ,   dtype=jnp.floataa						)

								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					jax.random.split(a						)
								__UpperCamelCase	:   Any			   =					{"params": params_rng, "dropout": dropout_rng}

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







	def    _lowerCamelCase				(					self		:List[str]						)		-> str:
								__UpperCamelCase	:   Optional[Any]			   =					self.block_out_channels
								__UpperCamelCase	:   str			   =					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.
								__UpperCamelCase	:   int			   =					self.num_attention_heads or self.attention_head_dim

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

								# time
								__UpperCamelCase	:   str			   =					FlaxTimesteps(
								    block_out_channels[0]     ,   flip_sin_to_cos=self.flip_sin_to_cos     ,   freq_shift=self.config.freq_shift						)
								__UpperCamelCase	:   str			   =					FlaxTimestepEmbedding(a     ,   dtype=self.dtype						)

								__UpperCamelCase	:   int			   =					self.only_cross_attention
								if isinstance(a     ,   a						):
															__UpperCamelCase	:   Tuple			   =					(only_cross_attention,) * len(self.down_block_types						)

								if isinstance(a     ,   a						):
															__UpperCamelCase	:   Union[str, Any]			   =					(num_attention_heads,) * len(self.down_block_types						)

								# down
								__UpperCamelCase	:   Tuple			   =					[]
								__UpperCamelCase	:   Dict			   =					block_out_channels[0]
								for i, down_block_type in enumerate(self.down_block_types						):
															__UpperCamelCase	:   List[str]			   =					output_channel
															__UpperCamelCase	:   Optional[int]			   =					block_out_channels[i]
															__UpperCamelCase	:   Union[str, Any]			   =					i == len(a						) - 1

															if down_block_type == "CrossAttnDownBlock2D":
																						__UpperCamelCase	:   Any			   =					FlaxCrossAttnDownBlockaD(
																						    in_channels=a     ,   out_channels=a     ,   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:
																						__UpperCamelCase	:   int			   =					FlaxDownBlockaD(
																						    in_channels=a     ,   out_channels=a     ,   dropout=self.dropout     ,   num_layers=self.layers_per_block     ,   add_downsample=not is_final_block     ,   dtype=self.dtype     ,   )

															down_blocks.append(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					down_blocks

								# mid
								__UpperCamelCase	:   Union[str, Any]			   =					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
								__UpperCamelCase	:   Dict			   =					[]
								__UpperCamelCase	:   Union[str, Any]			   =					list(reversed(a						)						)
								__UpperCamelCase	:   Dict			   =					list(reversed(a						)						)
								__UpperCamelCase	:   Any			   =					list(reversed(a						)						)
								__UpperCamelCase	:   Dict			   =					reversed_block_out_channels[0]
								for i, up_block_type in enumerate(self.up_block_types						):
															__UpperCamelCase	:   Union[str, Any]			   =					output_channel
															__UpperCamelCase	:   Union[str, Any]			   =					reversed_block_out_channels[i]
															__UpperCamelCase	:   List[Any]			   =					reversed_block_out_channels[min(i + 1     ,   len(a						) - 1						)]

															__UpperCamelCase	:   Union[str, Any]			   =					i == len(a						) - 1

															if up_block_type == "CrossAttnUpBlock2D":
																						__UpperCamelCase	:   List[Any]			   =					FlaxCrossAttnUpBlockaD(
																						    in_channels=a     ,   out_channels=a     ,   prev_output_channel=a     ,   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:
																						__UpperCamelCase	:   str			   =					FlaxUpBlockaD(
																						    in_channels=a     ,   out_channels=a     ,   prev_output_channel=a     ,   num_layers=self.layers_per_block + 1     ,   add_upsample=not is_final_block     ,   dropout=self.dropout     ,   dtype=self.dtype     ,   )

															up_blocks.append(a						)
															__UpperCamelCase	:   int			   =					output_channel
								__UpperCamelCase	:   Dict			   =					up_blocks

								# out
								__UpperCamelCase	:   Union[str, Any]			   =					nn.GroupNorm(num_groups=3_2     ,   epsilon=1E-5						)
								__UpperCamelCase	:   Tuple			   =					nn.Conv(
								    self.out_channels     ,   kernel_size=(3, 3)     ,   strides=(1, 1)     ,   padding=((1, 1), (1, 1))     ,   dtype=self.dtype     ,   )







	def __call__(					self		:Dict     ,   a		:str     ,   a		:Dict     ,   a		:List[str]     ,   a		:str=None     ,   a		:List[str]=None     ,   a		:bool = True     ,   a		:bool = False     ,   )		-> Union[FlaxUNetaDConditionOutput, Tuple]:
								# 1. time
								if not isinstance(a     ,   jnp.ndarray						):
															__UpperCamelCase	:   Tuple			   =					jnp.array([timesteps]     ,   dtype=jnp.intaa						)
								elif isinstance(a     ,   jnp.ndarray						) and len(timesteps.shape						) == 0:
															__UpperCamelCase	:   List[Any]			   =					timesteps.astype(dtype=jnp.floataa						)
															__UpperCamelCase	:   Any			   =					jnp.expand_dims(a     ,   0						)

								__UpperCamelCase	:   Union[str, Any]			   =					self.time_proj(a						)
								__UpperCamelCase	:   List[str]			   =					self.time_embedding(a						)

								# 2. pre-process
								__UpperCamelCase	:   Tuple			   =					jnp.transpose(a     ,   (0, 2, 3, 1)						)
								__UpperCamelCase	:   Tuple			   =					self.conv_in(a						)

								# 3. down
								__UpperCamelCase	:   Optional[int]			   =					(sample,)
								for down_block in self.down_blocks:
															if isinstance(a     ,   a						):
																						__UpperCamelCase   ,						__UpperCamelCase	:   int			   =					down_block(a     ,   a     ,   a     ,   deterministic=not train						)
															else:
																						__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					down_block(a     ,   a     ,   deterministic=not train						)
															down_block_res_samples += res_samples

								if down_block_additional_residuals is not None:
															__UpperCamelCase	:   Optional[Any]			   =					()

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

															__UpperCamelCase	:   Optional[int]			   =					new_down_block_res_samples

								# 4. mid
								__UpperCamelCase	:   Tuple			   =					self.mid_block(a     ,   a     ,   a     ,   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:
															__UpperCamelCase	:   Optional[int]			   =					down_block_res_samples[-(self.layers_per_block + 1) :]
															__UpperCamelCase	:   str			   =					down_block_res_samples[: -(self.layers_per_block + 1)]
															if isinstance(a     ,   a						):
																						__UpperCamelCase	:   Union[str, Any]			   =					up_block(
																						    a     ,   temb=a     ,   encoder_hidden_states=a     ,   res_hidden_states_tuple=a     ,   deterministic=not train     ,   )
															else:
																						__UpperCamelCase	:   Tuple			   =					up_block(a     ,   temb=a     ,   res_hidden_states_tuple=a     ,   deterministic=not train						)

        # 6. post-process
								__UpperCamelCase	:   List[str]			   =					self.conv_norm_out(a						)
								__UpperCamelCase	:   Dict			   =					nn.silu(a						)
								__UpperCamelCase	:   Tuple			   =					self.conv_out(a						)
								__UpperCamelCase	:   Dict			   =					jnp.transpose(a     ,   (0, 3, 1, 2)						)

								if not return_dict:
															return (sample,)

								return FlaxUNetaDConditionOutput(sample=a						)

class  lowerCamelCase__							:




	'''simple docstring'''




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







	def    _lowerCamelCase				(					self		:Any     ,   a		:list[str]						)		-> None:
								for word in words:
															self.insert(a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str						)		-> None:
								__UpperCamelCase	:   Dict			   =					self
								for char in word:
															if char not in curr.nodes:
																						__UpperCamelCase	:   List[Any]			   =					TrieNode()
															__UpperCamelCase	:   List[Any]			   =					curr.nodes[char]
								__UpperCamelCase	:   Union[str, Any]			   =					True







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> bool:
								__UpperCamelCase	:   Union[str, Any]			   =					self
								for char in word:
															if char not in curr.nodes:
																						return False
															__UpperCamelCase	:   Union[str, Any]			   =					curr.nodes[char]
								return curr.is_leaf







	def    _lowerCamelCase				(					self		:Any     ,   a		:str						)		-> None:

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

								_delete(self     ,   a     ,   0						)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			TrieNode						,  _lowerCamelCase :			str)					->    None:






							'''simple docstring'''





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

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




def 	_SCREAMING_SNAKE_CASE					(		)					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					"banana bananas bandana band apple all beast".split()
							__UpperCamelCase	:   Union[str, Any]			   =					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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			bool)					->    None:






							'''simple docstring'''





							print(str(_lowerCamelCase)						,  "works!" if passes else "doesn't work :(")




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							assert test_trie()




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							print_results("Testing trie functionality"						,  test_trie())


if __name__ == "__main__":
		main()