



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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			"DiagonalGaussianDistribution"






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				True






    @register_to_config
    def __init__(	self,		lowerCamelCase__ = 3,		lowerCamelCase__ = 3,		lowerCamelCase__ = ("DownEncoderBlock2D",),		lowerCamelCase__ = ("UpDecoderBlock2D",),		lowerCamelCase__ = (64,),		lowerCamelCase__ = 1,		lowerCamelCase__ = "silu",		lowerCamelCase__ = 4,		lowerCamelCase__ = 32,		lowerCamelCase__ = 32,		lowerCamelCase__ = 0.1_8215,		):
        super().__init__()

        # pass init params to Encoder
        A			:	Dict										=       Encoder(
            in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		down_block_types=lowerCamelCase__,		block_out_channels=lowerCamelCase__,		layers_per_block=lowerCamelCase__,		act_fn=lowerCamelCase__,		norm_num_groups=lowerCamelCase__,		double_z=lowerCamelCase__,		)

        # pass init params to Decoder
        A			:	Any										=       Decoder(
            in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		up_block_types=lowerCamelCase__,		block_out_channels=lowerCamelCase__,		layers_per_block=lowerCamelCase__,		norm_num_groups=lowerCamelCase__,		act_fn=lowerCamelCase__,		)

        A			:	str										=       nn.Convad(2 * latent_channels,		2 * latent_channels,		1		)
        A			:	Tuple										=       nn.Convad(lowerCamelCase__,		lowerCamelCase__,		1		)

        A			:	Optional[Any]										=       False
        A			:	Dict										=       False

        # only relevant if vae tiling is enabled
        A			:	List[str]										=       self.config.sample_size
        A			:	Union[str, Any]										=       (
            self.config.sample_size[0]
            if isinstance(self.config.sample_size,		(list, tuple)		)
            else self.config.sample_size
        )
        A			:	int										=       int(sample_size / (2 ** (len(self.config.block_out_channels		) - 1))		)
        A			:	str										=       0.25






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=False		):
        if isinstance(lowerCamelCase__,		(Encoder, Decoder)		):
            A			:	Union[str, Any]										=       value






    def       _lowerCAmelCase (	self,		lowerCamelCase__ = True		):
        A			:	Optional[Any]										=       use_tiling






    def       _lowerCAmelCase (	self		):
        self.enable_tiling(lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       True






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       False






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       {}

        def fn_recursive_add_processors(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                A			:	Optional[int]										=       module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f'''{name}.{sub_name}''',		lowerCamelCase__,		lowerCamelCase__		)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)

        return processors






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       len(self.attn_processors.keys()		)

        if isinstance(lowerCamelCase__,		lowerCamelCase__		) and len(lowerCamelCase__		) != count:
            raise ValueError(
                f'''A dict of processors was passed, but the number of processors {len(lowerCamelCase__		)} does not match the'''
                f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''		)

        def fn_recursive_attn_processor(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                    module.set_processor(lowerCamelCase__		)
                else:
                    module.set_processor(processor.pop(f'''{name}.processor'''		)		)

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f'''{name}.{sub_name}''',		lowerCamelCase__,		lowerCamelCase__		)

        for name, module in self.named_children():
            fn_recursive_attn_processor(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        self.set_attn_processor(AttnProcessor()		)






    @apply_forward_hook
    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            return self.tiled_encode(lowerCamelCase__,		return_dict=lowerCamelCase__		)

        if self.use_slicing and x.shape[0] > 1:
            A			:	str										=       [self.encoder(lowerCamelCase__		) for x_slice in x.split(1		)]
            A			:	int										=       torch.cat(lowerCamelCase__		)
        else:
            A			:	List[str]										=       self.encoder(lowerCamelCase__		)

        A			:	str										=       self.quant_conv(lowerCamelCase__		)
        A			:	List[str]										=       DiagonalGaussianDistribution(lowerCamelCase__		)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
            return self.tiled_decode(lowerCamelCase__,		return_dict=lowerCamelCase__		)

        A			:	str										=       self.post_quant_conv(lowerCamelCase__		)
        A			:	str										=       self.decoder(lowerCamelCase__		)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=lowerCamelCase__		)






    @apply_forward_hook
    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_slicing and z.shape[0] > 1:
            A			:	int										=       [self._decode(lowerCamelCase__		).sample for z_slice in z.split(1		)]
            A			:	Any										=       torch.cat(lowerCamelCase__		)
        else:
            A			:	Union[str, Any]										=       self._decode(lowerCamelCase__		).sample

        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Tuple										=       min(a.shape[2],		b.shape[2],		lowerCamelCase__		)
        for y in range(lowerCamelCase__		):
            A			:	int										=       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
        return b






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[int]										=       min(a.shape[3],		b.shape[3],		lowerCamelCase__		)
        for x in range(lowerCamelCase__		):
            A			:	Optional[Any]										=       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
        return b






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        A			:	List[str]										=       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
        A			:	int										=       int(self.tile_latent_min_size * self.tile_overlap_factor		)
        A			:	List[str]										=       self.tile_latent_min_size - blend_extent

        # Split the image into 512x512 tiles and encode them separately.
        A			:	Union[str, Any]										=       []
        for i in range(0,		x.shape[2],		lowerCamelCase__		):
            A			:	Optional[int]										=       []
            for j in range(0,		x.shape[3],		lowerCamelCase__		):
                A			:	Tuple										=       x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                A			:	Dict										=       self.encoder(lowerCamelCase__		)
                A			:	Optional[Any]										=       self.quant_conv(lowerCamelCase__		)
                row.append(lowerCamelCase__		)
            rows.append(lowerCamelCase__		)
        A			:	Any										=       []
        for i, row in enumerate(lowerCamelCase__		):
            A			:	Dict										=       []
            for j, tile in enumerate(lowerCamelCase__		):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    A			:	Dict										=       self.blend_v(rows[i - 1][j],		lowerCamelCase__,		lowerCamelCase__		)
                if j > 0:
                    A			:	List[Any]										=       self.blend_h(row[j - 1],		lowerCamelCase__,		lowerCamelCase__		)
                result_row.append(tile[:, :, :row_limit, :row_limit]		)
            result_rows.append(torch.cat(lowerCamelCase__,		dim=3		)		)

        A			:	Any										=       torch.cat(lowerCamelCase__,		dim=2		)
        A			:	Optional[int]										=       DiagonalGaussianDistribution(lowerCamelCase__		)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        A			:	List[Any]										=       int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)		)
        A			:	Any										=       int(self.tile_sample_min_size * self.tile_overlap_factor		)
        A			:	int										=       self.tile_sample_min_size - blend_extent

        # Split z into overlapping 64x64 tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        A			:	Any										=       []
        for i in range(0,		z.shape[2],		lowerCamelCase__		):
            A			:	Union[str, Any]										=       []
            for j in range(0,		z.shape[3],		lowerCamelCase__		):
                A			:	List[str]										=       z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                A			:	Union[str, Any]										=       self.post_quant_conv(lowerCamelCase__		)
                A			:	Union[str, Any]										=       self.decoder(lowerCamelCase__		)
                row.append(lowerCamelCase__		)
            rows.append(lowerCamelCase__		)
        A			:	Tuple										=       []
        for i, row in enumerate(lowerCamelCase__		):
            A			:	Tuple										=       []
            for j, tile in enumerate(lowerCamelCase__		):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    A			:	Any										=       self.blend_v(rows[i - 1][j],		lowerCamelCase__,		lowerCamelCase__		)
                if j > 0:
                    A			:	List[str]										=       self.blend_h(row[j - 1],		lowerCamelCase__,		lowerCamelCase__		)
                result_row.append(tile[:, :, :row_limit, :row_limit]		)
            result_rows.append(torch.cat(lowerCamelCase__,		dim=3		)		)

        A			:	str										=       torch.cat(lowerCamelCase__,		dim=2		)
        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		):
        A			:	List[str]										=       sample
        A			:	Optional[int]										=       self.encode(lowerCamelCase__		).latent_dist
        if sample_posterior:
            A			:	Optional[int]										=       posterior.sample(generator=lowerCamelCase__		)
        else:
            A			:	int										=       posterior.mode()
        A			:	Tuple										=       self.decode(lowerCamelCase__		).sample

        if not return_dict:
            return (dec,)

        return DecoderOutput(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


