



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			torch.FloatTensor






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @register_to_config
    def __init__(	self,		lowerCamelCase__ = 6_5536,		lowerCamelCase__ = None,		lowerCamelCase__ = 2,		lowerCamelCase__ = 2,		lowerCamelCase__ = 0,		lowerCamelCase__ = "fourier",		lowerCamelCase__ = True,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),		lowerCamelCase__ = "UNetMidBlock1D",		lowerCamelCase__ = None,		lowerCamelCase__ = (32, 32, 64),		lowerCamelCase__ = None,		lowerCamelCase__ = 8,		lowerCamelCase__ = 1,		lowerCamelCase__ = False,		):
        super().__init__()
        A			:	int										=       sample_size

        # time
        if time_embedding_type == "fourier":
            A			:	str										=       GaussianFourierProjection(
                embedding_size=8,		set_W_to_weight=lowerCamelCase__,		log=lowerCamelCase__,		flip_sin_to_cos=lowerCamelCase__		)
            A			:	List[str]										=       2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            A			:	Any										=       Timesteps(
                block_out_channels[0],		flip_sin_to_cos=lowerCamelCase__,		downscale_freq_shift=lowerCamelCase__		)
            A			:	Optional[Any]										=       block_out_channels[0]

        if use_timestep_embedding:
            A			:	List[Any]										=       block_out_channels[0] * 4
            A			:	Optional[int]										=       TimestepEmbedding(
                in_channels=lowerCamelCase__,		time_embed_dim=lowerCamelCase__,		act_fn=lowerCamelCase__,		out_dim=block_out_channels[0],		)

        A			:	Any										=       nn.ModuleList([]		)
        A			:	List[Any]										=       None
        A			:	List[str]										=       nn.ModuleList([]		)
        A			:	Optional[int]										=       None

        # down
        A			:	int										=       in_channels
        for i, down_block_type in enumerate(lowerCamelCase__		):
            A			:	Tuple										=       output_channel
            A			:	Union[str, Any]										=       block_out_channels[i]

            if i == 0:
                input_channel += extra_in_channels

            A			:	int										=       i == len(lowerCamelCase__		) - 1

            A			:	List[Any]										=       get_down_block(
                lowerCamelCase__,		num_layers=lowerCamelCase__,		in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		temb_channels=block_out_channels[0],		add_downsample=not is_final_block or downsample_each_block,		)
            self.down_blocks.append(lowerCamelCase__		)

        # mid
        A			:	List[str]										=       get_mid_block(
            lowerCamelCase__,		in_channels=block_out_channels[-1],		mid_channels=block_out_channels[-1],		out_channels=block_out_channels[-1],		embed_dim=block_out_channels[0],		num_layers=lowerCamelCase__,		add_downsample=lowerCamelCase__,		)

        # up
        A			:	int										=       list(reversed(lowerCamelCase__		)		)
        A			:	str										=       reversed_block_out_channels[0]
        if out_block_type is None:
            A			:	Optional[int]										=       out_channels
        else:
            A			:	str										=       block_out_channels[0]

        for i, up_block_type in enumerate(lowerCamelCase__		):
            A			:	Tuple										=       output_channel
            A			:	Union[str, Any]										=       (
                reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__		) - 1 else final_upsample_channels
            )

            A			:	Any										=       i == len(lowerCamelCase__		) - 1

            A			:	str										=       get_up_block(
                lowerCamelCase__,		num_layers=lowerCamelCase__,		in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		temb_channels=block_out_channels[0],		add_upsample=not is_final_block,		)
            self.up_blocks.append(lowerCamelCase__		)
            A			:	str										=       output_channel

        # out
        A			:	int										=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,		32		)
        A			:	Any										=       get_out_block(
            out_block_type=lowerCamelCase__,		num_groups_out=lowerCamelCase__,		embed_dim=block_out_channels[0],		out_channels=lowerCamelCase__,		act_fn=lowerCamelCase__,		fc_dim=block_out_channels[-1] // 4,		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = True,		):
        A			:	str										=       timestep
        if not torch.is_tensor(lowerCamelCase__		):
            A			:	Any										=       torch.tensor([timesteps],		dtype=torch.long,		device=sample.device		)
        elif torch.is_tensor(lowerCamelCase__		) and len(timesteps.shape		) == 0:
            A			:	Dict										=       timesteps[None].to(sample.device		)

        A			:	Any										=       self.time_proj(lowerCamelCase__		)
        if self.config.use_timestep_embedding:
            A			:	Any										=       self.time_mlp(lowerCamelCase__		)
        else:
            A			:	List[Any]										=       timestep_embed[..., None]
            A			:	Dict										=       timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
            A			:	str										=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

        # 2. down
        A			:	Optional[Any]										=       ()
        for downsample_block in self.down_blocks:
            A					,							A			:	str										=       downsample_block(hidden_states=lowerCamelCase__,		temb=lowerCamelCase__		)
            down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
            A			:	Optional[Any]										=       self.mid_block(lowerCamelCase__,		lowerCamelCase__		)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks		):
            A			:	Dict										=       down_block_res_samples[-1:]
            A			:	Optional[int]										=       down_block_res_samples[:-1]
            A			:	Any										=       upsample_block(lowerCamelCase__,		res_hidden_states_tuple=lowerCamelCase__,		temb=lowerCamelCase__		)

        # 5. post-process
        if self.out_block:
            A			:	str										=       self.out_block(lowerCamelCase__,		lowerCamelCase__		)

        if not return_dict:
            return (sample,)

        return UNetaDOutput(sample=lowerCamelCase__		)







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [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],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


