


'''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 						UpperCAmelCase_ (    _lowercase						):
     lowerCamelCase							:      jnp.ndarray
     lowerCamelCase							:      jnp.ndarray




class 						UpperCAmelCase_ (    nn.Module						):
     lowerCamelCase							:      int
     lowerCamelCase							:      Tuple[int]            =	(16, 32, 96, 256)
     lowerCamelCase							:      jnp.dtype            =	jnp.floataa





     def 				__UpperCAmelCase (					self			: Any       )							->    Dict:
        lowerCAmelCase	=     nn.Conv(
            self.block_out_channels[0]       , kernel_size=(3, 3)       , padding=((1, 1), (1, 1))       , dtype=self.dtype       , )

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

        lowerCAmelCase	=     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			: Tuple       , UpperCAmelCase__			: int       )							->    Any:
        lowerCAmelCase	=     self.conv_in(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     nn.silu(__SCREAMING_SNAKE_CASE       )

        for block in self.blocks:
           lowerCAmelCase	=     block(__SCREAMING_SNAKE_CASE       )
           lowerCAmelCase	=     nn.silu(__SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     self.conv_out(__SCREAMING_SNAKE_CASE       )

        return embedding




@flax_register_to_config
class 						UpperCAmelCase_ (    nn.Module			,      _lowercase			,      _lowercase						):
     lowerCamelCase							:      int            =	32
     lowerCamelCase							:      int            =	4
     lowerCamelCase							:      Tuple[str]            =	(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     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							:      str            =	"rgb"
     lowerCamelCase							:      Tuple[int]            =	(16, 32, 96, 256)





     def 				__UpperCAmelCase (					self			: Tuple       , UpperCAmelCase__			: Optional[int]       )							->    FrozenDict:
        lowerCAmelCase	=     (1, self.in_channels, self.sample_size, self.sample_size)
        lowerCAmelCase	=     jnp.zeros(__SCREAMING_SNAKE_CASE       , dtype=jnp.floataa       )
        lowerCAmelCase	=     jnp.ones((1,)       , dtype=jnp.intaa       )
        lowerCAmelCase	=     jnp.zeros((1, 1, self.cross_attention_dim)       , dtype=jnp.floataa       )
        lowerCAmelCase	=     (1, 3, self.sample_size * 8, self.sample_size * 8)
        lowerCAmelCase	=     jnp.zeros(__SCREAMING_SNAKE_CASE       , dtype=jnp.floataa       )

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

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





     def 				__UpperCAmelCase (					self			: Tuple       )							->    str:
        lowerCAmelCase	=     self.block_out_channels
        lowerCAmelCase	=     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	=     self.num_attention_heads or self.attention_head_dim

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

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

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

        lowerCAmelCase	=     self.only_cross_attention
        if isinstance(__SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       ):
           lowerCAmelCase	=     (only_cross_attention,) * len(self.down_block_types       )

        if isinstance(__SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       ):
           lowerCAmelCase	=     (num_attention_heads,) * len(self.down_block_types       )

        # down
        lowerCAmelCase	=     []
        lowerCAmelCase	=     []

        lowerCAmelCase	=     block_out_channels[0]

        lowerCAmelCase	=     nn.Conv(
            __SCREAMING_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(__SCREAMING_SNAKE_CASE       )

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

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

           down_blocks.append(__SCREAMING_SNAKE_CASE       )

           for _ in range(self.layers_per_block       ):
              lowerCAmelCase	=     nn.Conv(
                  __SCREAMING_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(__SCREAMING_SNAKE_CASE       )

           if not is_final_block:
              lowerCAmelCase	=     nn.Conv(
                  __SCREAMING_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(__SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     down_blocks
        lowerCAmelCase	=     controlnet_down_blocks

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

        lowerCAmelCase	=     nn.Conv(
            __SCREAMING_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			: int       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Optional[int] = 1.0       , UpperCAmelCase__			: Optional[int] = True       , UpperCAmelCase__			: int = False       , )							->    Union[FlaxControlNetOutput, Tuple]:
        lowerCAmelCase	=     self.controlnet_conditioning_channel_order
        if channel_order == "bgr":
           lowerCAmelCase	=     jnp.flip(__SCREAMING_SNAKE_CASE       , axis=1       )

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

        lowerCAmelCase	=     self.time_proj(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.time_embedding(__SCREAMING_SNAKE_CASE       )

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

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

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

        # 4. mid
        lowerCAmelCase	=     self.mid_block(__SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       , deterministic=not train       )

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

        lowerCAmelCase	=     controlnet_down_block_res_samples

        lowerCAmelCase	=     self.controlnet_mid_block(__SCREAMING_SNAKE_CASE       )

        # 6. scaling
        lowerCAmelCase	=     [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=__SCREAMING_SNAKE_CASE       , mid_block_res_sample=__SCREAMING_SNAKE_CASE       )







"""simple docstring"""




from __future__ import annotations

a		:       str     						=       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			graph
         # mapping node to its parent in resulting breadth first tree
         a__		=			{}
         a__		=			source_vertex



     def 			lowercase__  (							self   )		->     None:







         """simple docstring"""



         a__		=			{self.source_vertex}
         a__		=			None
         a__		=			[self.source_vertex]  # first in first out queue

         while queue:
             a__		=			queue.pop(0   )
             for adjacent_vertex in self.graph[vertex]:
                 if adjacent_vertex not in visited:
                     visited.add(__SCREAMING_SNAKE_CASE   )
                     a__		=			vertex
                     queue.append(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         if target_vertex == self.source_vertex:
             return self.source_vertex

         a__		=			self.parent.get(__SCREAMING_SNAKE_CASE   )
         if target_vertex_parent is None:
             a__		=			(
                 f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
             )
             raise ValueError(__SCREAMING_SNAKE_CASE   )

         return self.shortest_path(__SCREAMING_SNAKE_CASE   ) + f'->{target_vertex}'


if __name__ == "__main__":
    a		:       Optional[Any]     						=       Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))


