


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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 42






class       _UpperCamelCase    (					__snake_case    ,		__snake_case				):

				"""simple docstring"""







				@register_to_config
				def __init__(						self				,   a__ = 3				,   a__ = 3				,   a__ = ("DownEncoderBlock2D",)				,   a__ = ("UpDecoderBlock2D",)				,   a__ = (64,)				,   a__ = 1				,   a__ = "silu"				,   a__ = 3				,   a__ = 32				,   a__ = 256				,   a__ = 32				,   a__ = None				,   a__ = 0.1_82_15				,   a__ = "group"				,   )	->				Optional[Any]:
					super().__init__()

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

					A								=				vq_embed_dim if vq_embed_dim is not None else latent_channels

					A								=				nn.Convad(a__				,   a__				,   1   )
					A								=				VectorQuantizer(a__				,   a__				,   beta=0.25				,   remap=a__				,   sane_index_shape=a__   )
					A								=				nn.Convad(a__				,   a__				,   1   )

					# pass init params to Decoder
					A								=				Decoder(
					    in_channels=a__				,   out_channels=a__				,   up_block_types=a__				,   block_out_channels=a__				,   layers_per_block=a__				,   act_fn=a__				,   norm_num_groups=a__				,   norm_type=a__				,   )

				@apply_forward_hook
				def      _UpperCAmelCase					(						self				,   a__				,   a__ = True   )	->				VQEncoderOutput:
					A								=				self.encoder(a__   )
					A								=				self.quant_conv(a__   )

					if not return_dict:
						return (h,)

					return VQEncoderOutput(latents=a__   )

				@apply_forward_hook
				def      _UpperCAmelCase					(						self				,   a__				,   a__ = False				,   a__ = True   )	->				Union[DecoderOutput, torch.FloatTensor]:
					# also go through quantization layer
					if not force_not_quantize:
						A					,			A					,			A								=				self.quantize(a__   )
					else:
						A								=				h
					A								=				self.post_quant_conv(a__   )
					A								=				self.decoder(a__				,   quant if self.config.norm_type == """spatial""" else None   )

					if not return_dict:
						return (dec,)

					return DecoderOutput(sample=a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = True   )	->				Union[DecoderOutput, torch.FloatTensor]:
					A								=				sample
					A								=				self.encode(a__   ).latents
					A								=				self.decode(a__   ).sample

					if not return_dict:
						return (dec,)

					return DecoderOutput(sample=a__   )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



