
"""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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   42
					_SCREAMING_SNAKE_CASE       :					Dict     							=   42







class 							__magic_name__     (		nn.Module   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   42
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   (16, 32, 96, 256)
					_SCREAMING_SNAKE_CASE       :					Dict     							=   jnp.floataa





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       nn.Conv(
												    self.block_out_channels[0]							,						kernel_size=(3, 3)							,						padding=((1, 1), (1, 1))							,						dtype=self.dtype							,						)

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

												__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_   :							List[Any]    ):
												__snake_case     			=       self.conv_in(_lowerCAmelCase    )
												__snake_case     			=       nn.silu(_lowerCAmelCase    )

												for block in self.blocks:
																			__snake_case     			=       block(_lowerCAmelCase    )
																			__snake_case     			=       nn.silu(_lowerCAmelCase    )

												__snake_case     			=       self.conv_out(_lowerCAmelCase    )

												return embedding







@flax_register_to_config
class 							__magic_name__     (		nn.Module      ,		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   32
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   4
					_SCREAMING_SNAKE_CASE       :					Any     							=   (
					    'CrossAttnDownBlock2D',
					    'CrossAttnDownBlock2D',
					    'CrossAttnDownBlock2D',
					    'DownBlock2D',
					)
					_SCREAMING_SNAKE_CASE       :					int     							=   False
					_SCREAMING_SNAKE_CASE       :					int     							=   (320, 640, 1280, 1280)
					_SCREAMING_SNAKE_CASE       :					str     							=   2
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   8
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   None
					_SCREAMING_SNAKE_CASE       :					str     							=   1280
					_SCREAMING_SNAKE_CASE       :					int     							=   0.0
					_SCREAMING_SNAKE_CASE       :					Dict     							=   False
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   jnp.floataa
					_SCREAMING_SNAKE_CASE       :					str     							=   True
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   0
					_SCREAMING_SNAKE_CASE       :					Dict     							=   'rgb'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   (16, 32, 96, 256)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Any    ):
												# init input tensors
												__snake_case     			=       (1, self.in_channels, self.sample_size, self.sample_size)
												__snake_case     			=       jnp.zeros(_lowerCAmelCase							,						dtype=jnp.floataa    )
												__snake_case     			=       jnp.ones((1,)							,						dtype=jnp.intaa    )
												__snake_case     			=       jnp.zeros((1, 1, self.cross_attention_dim)							,						dtype=jnp.floataa    )
												__snake_case     			=       (1, 3, self.sample_size * 8, self.sample_size * 8)
												__snake_case     			=       jnp.zeros(_lowerCAmelCase							,						dtype=jnp.floataa    )

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

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





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.block_out_channels
												__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.
												__snake_case     			=       self.num_attention_heads or self.attention_head_dim

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

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

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

												__snake_case     			=       self.only_cross_attention
												if isinstance(_lowerCAmelCase							,						_lowerCAmelCase    ):
																			__snake_case     			=       (only_cross_attention,) * len(self.down_block_types    )

												if isinstance(_lowerCAmelCase							,						_lowerCAmelCase    ):
																			__snake_case     			=       (num_attention_heads,) * len(self.down_block_types    )

												# down
												__snake_case     			=       []
												__snake_case     			=       []

												__snake_case     			=       block_out_channels[0]

												__snake_case     			=       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    ):
																			__snake_case     			=       output_channel
																			__snake_case     			=       block_out_channels[i]
																			__snake_case     			=       i == len(_lowerCAmelCase    ) - 1

																			if down_block_type == "CrossAttnDownBlock2D":
																										__snake_case     			=       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:
																										__snake_case     			=       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    ):
																										__snake_case     			=       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:
																										__snake_case     			=       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    )

												__snake_case     			=       down_blocks
												__snake_case     			=       controlnet_down_blocks

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

												__snake_case     			=       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							,						snake_case_   :							int							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							Any = 1.0							,						snake_case_   :							Any = True							,						snake_case_   :							Tuple = False							,						):
												__snake_case     			=       self.controlnet_conditioning_channel_order
												if channel_order == "bgr":
																			__snake_case     			=       jnp.flip(_lowerCAmelCase							,						axis=1    )

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

												__snake_case     			=       self.time_proj(_lowerCAmelCase    )
												__snake_case     			=       self.time_embedding(_lowerCAmelCase    )

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

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

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

												# 4. mid
												__snake_case     			=       self.mid_block(_lowerCAmelCase							,						_lowerCAmelCase							,						_lowerCAmelCase							,						deterministic=not train    )

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

												__snake_case     			=       controlnet_down_block_res_samples

												__snake_case     			=       self.controlnet_mid_block(_lowerCAmelCase    )

												# 6. scaling
												__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=_lowerCAmelCase							,						mid_block_res_sample=_lowerCAmelCase    )


"""simple docstring"""







from __future__ import annotations

_SCREAMING_SNAKE_CASE	       =      {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							dict[str, list[str]]							,						snake_case_   :							str    ):
												__snake_case     			=       graph
												# mapping node to its parent in resulting breadth first tree
												__snake_case     			=       {}
												__snake_case     			=       source_vertex





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {self.source_vertex}
												__snake_case     			=       None
												__snake_case     			=       [self.source_vertex]  # first in first out queue

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





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												if target_vertex == self.source_vertex:
																			return self.source_vertex

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

												return self.shortest_path(snake_case_    ) + F'''->{target_vertex}'''


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      Graph(graph, """G""")
		g.breath_first_search()
		print(g.shortest_path("""D"""))
		print(g.shortest_path("""G"""))
		print(g.shortest_path("""Foo"""))
