


'''simple docstring'''



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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       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							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)








'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       len(UpperCamelCase__   )
			UpperCAmelCase__					:   List[Any]       				=       sum(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       [[False for x in range(s + 1   )] for y in range(n + 1   )]

			for i in range(1	, n + 1   ):
						UpperCAmelCase__					:   int       				=       True

			for i in range(1	, s + 1   ):
						UpperCAmelCase__					:   Dict       				=       False

			for i in range(1	, n + 1   ):
						for j in range(1	, s + 1   ):
									UpperCAmelCase__					:   int       				=       dp[i][j - 1]

									if arr[i - 1] <= j:
												UpperCAmelCase__					:   str       				=       dp[i][j] or dp[i - 1][j - arr[i - 1]]

			for j in range(int(s / 2   )	, -1	, -1   ):
						if dp[n][j] is True:
									UpperCAmelCase__					:   int       				=       s - 2 * j
									break

			return diff