
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        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: torch.FloatTensor







class        lowerCAmelCase__      (   __lowercase		,	__lowercase	):

						@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 = 2_56       ,							a = 32       ,							a = None       ,							a = 0.1_8215       ,							a = "group"       ,							)					->      Any:


								'''simple docstring'''

								super().__init__()

								# pass init params to Encoder
								_UpperCamelCase						    =	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       ,							)

								_UpperCamelCase						    =	vq_embed_dim if vq_embed_dim is not None else latent_channels

								_UpperCamelCase						    =	nn.Convad(a       ,							a       ,							1				)
								_UpperCamelCase						    =	VectorQuantizer(a       ,							a       ,							beta=0.25       ,							remap=a       ,							sane_index_shape=a				)
								_UpperCamelCase						    =	nn.Convad(a       ,							a       ,							1				)

								# pass init params to Decoder
								_UpperCamelCase						    =	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  A_				(			self       ,							a       ,							a = True				)					->      VQEncoderOutput:


								'''simple docstring'''

								_UpperCamelCase						    =	self.encoder(a				)
								_UpperCamelCase						    =	self.quant_conv(a				)

								if not return_dict:
										return (h,)

								return VQEncoderOutput(latents=a				)

						@apply_forward_hook
						def  A_				(			self       ,							a       ,							a = False       ,							a = True				)					->      Union[DecoderOutput, torch.FloatTensor]:


								'''simple docstring'''

								if not force_not_quantize:
										_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	self.quantize(a				)
								else:
										_UpperCamelCase						    =	h
								_UpperCamelCase						    =	self.post_quant_conv(a				)
								_UpperCamelCase						    =	self.decoder(a       ,							quant if self.config.norm_type == """spatial""" else None				)

								if not return_dict:
										return (dec,)

								return DecoderOutput(sample=a				)







						def  A_				(			self       ,							a       ,							a = True				)					->      Union[DecoderOutput, torch.FloatTensor]:


								'''simple docstring'''

								_UpperCamelCase						    =	sample
								_UpperCamelCase						    =	self.encode(a				).latents
								_UpperCamelCase						    =	self.decode(a				).sample

								if not return_dict:
										return (dec,)

								return DecoderOutput(sample=a				)





lowerCamelCase__	=							[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



