



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      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   jnp.ndarray






@flax_register_to_config
class      UpperCamelCase    (						nn.Module       ,    _UpperCAmelCase       ,    _UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	32
	lowerCAmelCase	:   int				=	4
	lowerCAmelCase	:   int				=	4
	lowerCAmelCase	:   Tuple[str]				=	(
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	lowerCAmelCase	:   Tuple[str]				=	("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
	lowerCAmelCase	:   Union[bool, Tuple[bool]]				=	False
	lowerCAmelCase	:   Tuple[int]				=	(320, 640, 1_280, 1_280)
	lowerCAmelCase	:   int				=	2
	lowerCAmelCase	:   Union[int, Tuple[int]]				=	8
	lowerCAmelCase	:   Optional[Union[int, Tuple[int]]]				=	None
	lowerCAmelCase	:   int				=	1_280
	lowerCAmelCase	:   float				=	0.0
	lowerCAmelCase	:   bool				=	False
	lowerCAmelCase	:   jnp.dtype				=	jnp.floataa
	lowerCAmelCase	:   bool				=	True
	lowerCAmelCase	:   int				=	0
	lowerCAmelCase	:   bool				=	False



	def 			__A							(       self			,	UpperCAmelCase__		):
							# init input tensors
							A__							=       (1, self.in_channels, self.sample_size, self.sample_size)
							A__							=       jnp.zeros(UpperCAmelCase__			,	dtype=jnp.floataa		)
							A__							=       jnp.ones((1,)			,	dtype=jnp.intaa		)
							A__							=       jnp.zeros((1, 1, self.cross_attention_dim)			,	dtype=jnp.floataa		)

							A__						,       A__							=       jax.random.split(UpperCAmelCase__		)
							A__							=       {"params": params_rng, "dropout": dropout_rng}

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



	def 			__A							(       self		):
							A__							=       self.block_out_channels
							A__							=       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.
							A__							=       self.num_attention_heads or self.attention_head_dim

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

							# time
							A__							=       FlaxTimesteps(
							    block_out_channels[0]			,	flip_sin_to_cos=self.flip_sin_to_cos			,	freq_shift=self.config.freq_shift		)
							A__							=       FlaxTimestepEmbedding(UpperCAmelCase__			,	dtype=self.dtype		)

							A__							=       self.only_cross_attention
							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       (only_cross_attention,) * len(self.down_block_types		)

							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       (num_attention_heads,) * len(self.down_block_types		)

							# down
							A__							=       []
							A__							=       block_out_channels[0]
							for i, down_block_type in enumerate(self.down_block_types		):
													A__							=       output_channel
													A__							=       block_out_channels[i]
													A__							=       i == len(UpperCAmelCase__		) - 1

													if down_block_type == "CrossAttnDownBlock2D":
																			A__							=       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:
																			A__							=       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__		)
							A__							=       down_blocks

							# mid
							A__							=       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
							A__							=       []
							A__							=       list(reversed(UpperCAmelCase__		)		)
							A__							=       list(reversed(UpperCAmelCase__		)		)
							A__							=       list(reversed(UpperCAmelCase__		)		)
							A__							=       reversed_block_out_channels[0]
							for i, up_block_type in enumerate(self.up_block_types		):
													A__							=       output_channel
													A__							=       reversed_block_out_channels[i]
													A__							=       reversed_block_out_channels[min(i + 1			,	len(UpperCAmelCase__		) - 1		)]

													A__							=       i == len(UpperCAmelCase__		) - 1

													if up_block_type == "CrossAttnUpBlock2D":
																			A__							=       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:
																			A__							=       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__		)
													A__							=       output_channel
							A__							=       up_blocks

							# out
							A__							=       nn.GroupNorm(num_groups=32			,	epsilon=1e-5		)
							A__							=       nn.Conv(
							    self.out_channels			,	kernel_size=(3, 3)			,	strides=(1, 1)			,	padding=((1, 1), (1, 1))			,	dtype=self.dtype			,	)



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = False			,	):
							# 1. time
							if not isinstance(UpperCAmelCase__			,	jnp.ndarray		):
													A__							=       jnp.array([timesteps]			,	dtype=jnp.intaa		)
							elif isinstance(UpperCAmelCase__			,	jnp.ndarray		) and len(timesteps.shape		) == 0:
													A__							=       timesteps.astype(dtype=jnp.floataa		)
													A__							=       jnp.expand_dims(UpperCAmelCase__			,	0		)

							A__							=       self.time_proj(UpperCAmelCase__		)
							A__							=       self.time_embedding(UpperCAmelCase__		)

							# 2. pre-process
							A__							=       jnp.transpose(UpperCAmelCase__			,	(0, 2, 3, 1)		)
							A__							=       self.conv_in(UpperCAmelCase__		)

							# 3. down
							A__							=       (sample,)
							for down_block in self.down_blocks:
													if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
																			A__						,       A__							=       down_block(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	deterministic=not train		)
													else:
																			A__						,       A__							=       down_block(UpperCAmelCase__			,	UpperCAmelCase__			,	deterministic=not train		)
													down_block_res_samples += res_samples

							if down_block_additional_residuals is not None:
													A__							=       ()

													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,)

													A__							=       new_down_block_res_samples

							# 4. mid
							A__							=       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:
													A__							=       down_block_res_samples[-(self.layers_per_block + 1) :]
													A__							=       down_block_res_samples[: -(self.layers_per_block + 1)]
													if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
																			A__							=       up_block(
																			    UpperCAmelCase__			,	temb=UpperCAmelCase__			,	encoder_hidden_states=UpperCAmelCase__			,	res_hidden_states_tuple=UpperCAmelCase__			,	deterministic=not train			,	)
													else:
																			A__							=       up_block(UpperCAmelCase__			,	temb=UpperCAmelCase__			,	res_hidden_states_tuple=UpperCAmelCase__			,	deterministic=not train		)

        # 6. post-process
							A__							=       self.conv_norm_out(UpperCAmelCase__		)
							A__							=       nn.silu(UpperCAmelCase__		)
							A__							=       self.conv_out(UpperCAmelCase__		)
							A__							=       jnp.transpose(UpperCAmelCase__			,	(0, 3, 1, 2)		)

							if not return_dict:
													return (sample,)

							return FlaxUNetaDConditionOutput(sample=UpperCAmelCase__		)










from __future__ import annotations

from random import random






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None		):
							A__							=       value
							A__							=       random()
							A__							=       None
							A__							=       None



	def __repr__(       self		):
							from pprint import pformat

							if self.left is None and self.right is None:
													return F"""'{self.value}: {self.prior:.5}'"""
							else:
													return pformat(
													    {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}			,	indent=1		)



	def __str__(       self		):
							A__							=       str(self.value		) + " "
							A__							=       str(self.left or ""		)
							A__							=       str(self.right or ""		)
							return value + left + right





def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       tuple[Node | None, Node | None]:





						"""simple docstring"""





						if root is None:  # None tree is split into 2 Nones
												return None, None
						elif root.value is None:
												return None, None
						else:
												if value < root.value:
																		A__						,       A__							=       split(root.left			,							_A    )
																		return left, root
												else:
																		A__						,       A__							=       split(root.right			,							_A    )
																		return root, right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				Node | None    )->       Node | None:





						"""simple docstring"""





						if (not left) or (not right):  # If one node is None, return the other
												return left or right
						elif left.prior < right.prior:
												A__							=       merge(left.right			,							_A    )
												return left
						else:
												A__							=       merge(_A			,							right.left    )
												return right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__							=       Node(_A    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(merge(_A			,							_A    )			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__						,       A__							=       split(_A			,							value - 1    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None    )->       None:





						"""simple docstring"""





						if not root:  # None
												return
						else:
												inorder(root.left    )
												print(root.value			,							end=","    )
												inorder(root.right    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				str    )->       Node | None:





						"""simple docstring"""





						for arg in args.split():
												if arg[0] == "+":
																		A__							=       insert(_A			,							int(arg[1:]    )    )

												elif arg[0] == "-":
																		A__							=       erase(_A			,							int(arg[1:]    )    )

												else:
																		print("Unknown command"    )

						return root
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       None
						print(
						    "enter numbers to create a tree, + value to add value into treap, "
						    "- value to erase all nodes with value. 'q' to quit. "    )

						A__							=       input()
						while args != "q":
												A__							=       interact_treap(_A			,							_A    )
												print(_A    )
												A__							=       input()

						print("good by!"    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





