

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   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =42
    __UpperCamelCase    =42




class   UpperCamelCase			(  nn.Module ):
    __UpperCamelCase    =42
    __UpperCamelCase    =(16, 32, 96, 2_56)
    __UpperCamelCase    =jnp.floataa

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      nn.Conv(
         self.block_out_channels[0]				,     kernel_size=(3, 3)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )

     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(self.block_out_channels     ) - 1     ):
      SCREAMING_SNAKE_CASE		    =      self.block_out_channels[i]
      SCREAMING_SNAKE_CASE		    =      self.block_out_channels[i + 1]
      SCREAMING_SNAKE_CASE		    =      nn.Conv(
          snake_case__				,     kernel_size=(3, 3)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )
      blocks.append(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      nn.Conv(
          snake_case__				,     kernel_size=(3, 3)				,     strides=(2, 2)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )
      blocks.append(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      blocks

     SCREAMING_SNAKE_CASE		    =      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			:   List[str]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.conv_in(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      nn.silu(snake_case__     )

     for block in self.blocks:
      SCREAMING_SNAKE_CASE		    =      block(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      nn.silu(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.conv_out(snake_case__     )

     return embedding




@flax_register_to_config
class   UpperCamelCase			(  nn.Module						,      SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =32
    __UpperCamelCase    =4
    __UpperCamelCase    =(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    __UpperCamelCase    =False
    __UpperCamelCase    =(3_20, 6_40, 12_80, 12_80)
    __UpperCamelCase    =2
    __UpperCamelCase    =8
    __UpperCamelCase    =None
    __UpperCamelCase    =12_80
    __UpperCamelCase    =0.0
    __UpperCamelCase    =False
    __UpperCamelCase    =jnp.floataa
    __UpperCamelCase    =True
    __UpperCamelCase    =0
    __UpperCamelCase    ="rgb"
    __UpperCamelCase    =(16, 32, 96, 2_56)

    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   jax.random.KeyArray     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      (1, self.in_channels, self.sample_size, self.sample_size)
     SCREAMING_SNAKE_CASE		    =      jnp.zeros(snake_case__				,     dtype=jnp.floataa     )
     SCREAMING_SNAKE_CASE		    =      jnp.ones((1,)				,     dtype=jnp.intaa     )
     SCREAMING_SNAKE_CASE		    =      jnp.zeros((1, 1, self.cross_attention_dim)				,     dtype=jnp.floataa     )
     SCREAMING_SNAKE_CASE		    =      (1, 3, self.sample_size * 8, self.sample_size * 8)
     SCREAMING_SNAKE_CASE		    =      jnp.zeros(snake_case__				,     dtype=jnp.floataa     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {'params': params_rng, 'dropout': dropout_rng}

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

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.block_out_channels
     SCREAMING_SNAKE_CASE		    =      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.
     SCREAMING_SNAKE_CASE		    =      self.num_attention_heads or self.attention_head_dim

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

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

     SCREAMING_SNAKE_CASE		    =      FlaxControlNetConditioningEmbedding(
         conditioning_embedding_channels=block_out_channels[0]				,     block_out_channels=self.conditioning_embedding_out_channels				,     )

     SCREAMING_SNAKE_CASE		    =      self.only_cross_attention
     if isinstance(snake_case__				,     snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      (only_cross_attention,) * len(self.down_block_types     )

     if isinstance(snake_case__				,     snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      (num_attention_heads,) * len(self.down_block_types     )

     # down
     SCREAMING_SNAKE_CASE		    =      []
     SCREAMING_SNAKE_CASE		    =      []

     SCREAMING_SNAKE_CASE		    =      block_out_channels[0]

     SCREAMING_SNAKE_CASE		    =      nn.Conv(
         snake_case__				,     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(snake_case__     )

     for i, down_block_type in enumerate(self.down_block_types     ):
      SCREAMING_SNAKE_CASE		    =      output_channel
      SCREAMING_SNAKE_CASE		    =      block_out_channels[i]
      SCREAMING_SNAKE_CASE		    =      i == len(snake_case__     ) - 1

      if down_block_type == "CrossAttnDownBlock2D":
       SCREAMING_SNAKE_CASE		    =      FlaxCrossAttnDownBlockaD(
           in_channels=snake_case__				,     out_channels=snake_case__				,     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:
       SCREAMING_SNAKE_CASE		    =      FlaxDownBlockaD(
           in_channels=snake_case__				,     out_channels=snake_case__				,     dropout=self.dropout				,     num_layers=self.layers_per_block				,     add_downsample=not is_final_block				,     dtype=self.dtype				,     )

      down_blocks.append(snake_case__     )

      for _ in range(self.layers_per_block     ):
       SCREAMING_SNAKE_CASE		    =      nn.Conv(
           snake_case__				,     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(snake_case__     )

      if not is_final_block:
       SCREAMING_SNAKE_CASE		    =      nn.Conv(
           snake_case__				,     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(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      down_blocks
     SCREAMING_SNAKE_CASE		    =      controlnet_down_blocks

     # mid
     SCREAMING_SNAKE_CASE		    =      block_out_channels[-1]
     SCREAMING_SNAKE_CASE		    =      FlaxUNetMidBlockaDCrossAttn(
         in_channels=snake_case__				,     dropout=self.dropout				,     num_attention_heads=num_attention_heads[-1]				,     use_linear_projection=self.use_linear_projection				,     dtype=self.dtype				,     )

     SCREAMING_SNAKE_CASE		    =      nn.Conv(
         snake_case__				,     kernel_size=(1, 1)				,     padding='VALID'				,     kernel_init=nn.initializers.zeros_init()				,     bias_init=nn.initializers.zeros_init()				,     dtype=self.dtype				,     )




    def __call__(      self			:   str				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   Dict				,     snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   float = 1.0				,     snake_case__			:   bool = True				,     snake_case__			:   bool = False				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.controlnet_conditioning_channel_order
     if channel_order == "bgr":
      SCREAMING_SNAKE_CASE		    =      jnp.flip(snake_case__				,     axis=1     )

     # 1. time
     if not isinstance(snake_case__				,     jnp.ndarray     ):
      SCREAMING_SNAKE_CASE		    =      jnp.array([timesteps]				,     dtype=jnp.intaa     )
     elif isinstance(snake_case__				,     jnp.ndarray     ) and len(timesteps.shape     ) == 0:
      SCREAMING_SNAKE_CASE		    =      timesteps.astype(dtype=jnp.floataa     )
      SCREAMING_SNAKE_CASE		    =      jnp.expand_dims(snake_case__				,     0     )

     SCREAMING_SNAKE_CASE		    =      self.time_proj(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.time_embedding(snake_case__     )

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

     SCREAMING_SNAKE_CASE		    =      jnp.transpose(snake_case__				,     (0, 2, 3, 1)     )
     SCREAMING_SNAKE_CASE		    =      self.controlnet_cond_embedding(snake_case__     )
     sample += controlnet_cond

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

     # 4. mid
     SCREAMING_SNAKE_CASE		    =      self.mid_block(snake_case__				,     snake_case__				,     snake_case__				,     deterministic=not train     )

     # 5. contronet blocks
     SCREAMING_SNAKE_CASE		    =      ()
     for down_block_res_sample, controlnet_block in zip(snake_case__				,     self.controlnet_down_blocks     ):
      SCREAMING_SNAKE_CASE		    =      controlnet_block(snake_case__     )
      controlnet_down_block_res_samples += (down_block_res_sample,)

     SCREAMING_SNAKE_CASE		    =      controlnet_down_block_res_samples

     SCREAMING_SNAKE_CASE		    =      self.controlnet_mid_block(snake_case__     )

     # 6. scaling
     SCREAMING_SNAKE_CASE		    =      [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=snake_case__				,     mid_block_res_sample=snake_case__     )




from graphs.minimum_spanning_tree_kruskal import kruskal


def 							__lowerCAmelCase     (							)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      9
 SCREAMING_SNAKE_CASE		    =      [
     [0, 1, 4],
     [0, 7, 8],
     [1, 2, 8],
     [7, 8, 7],
     [7, 6, 1],
     [2, 8, 2],
     [8, 6, 6],
     [2, 3, 7],
     [2, 5, 4],
     [6, 5, 2],
     [3, 5, 14],
     [3, 4, 9],
     [5, 4, 10],
     [1, 7, 11],
 ]

 SCREAMING_SNAKE_CASE		    =      kruskal(_UpperCamelCase		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      [
     [7, 6, 1],
     [2, 8, 2],
     [6, 5, 2],
     [0, 1, 4],
     [2, 5, 4],
     [2, 3, 7],
     [0, 7, 8],
     [3, 4, 9],
 ]

 assert sorted(_UpperCamelCase						) == sorted(_UpperCamelCase						)

