
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_       :						Any     					=	logging.get_logger(__name__)

lowercase_       :						int     					=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""vit_msn"""
     def __init__(     self  ,     snake_case__=768  ,     snake_case__=12  ,     snake_case__=12  ,     snake_case__=3072  ,     snake_case__="gelu"  ,     snake_case__=0.0  ,     snake_case__=0.0  ,     snake_case__=0.02  ,     snake_case__=1E-06  ,     snake_case__=224  ,     snake_case__=16  ,     snake_case__=3  ,     snake_case__=True  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(**snake_case__				)

       _SCREAMING_SNAKE_CASE   :				int							    = hidden_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = num_hidden_layers
       _SCREAMING_SNAKE_CASE   :				str							    = num_attention_heads
       _SCREAMING_SNAKE_CASE   :				Tuple							    = intermediate_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = hidden_act
       _SCREAMING_SNAKE_CASE   :				Tuple							    = hidden_dropout_prob
       _SCREAMING_SNAKE_CASE   :				Dict							    = attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE   :				Tuple							    = initializer_range
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = layer_norm_eps
       _SCREAMING_SNAKE_CASE   :				List[str]							    = image_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = patch_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = num_channels
       _SCREAMING_SNAKE_CASE   :				str							    = qkv_bias





"""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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     @register_to_config
     def __init__(     self  ,     snake_case__ = 3  ,     snake_case__ = 3  ,     snake_case__ = ("DownEncoderBlock2D",)  ,     snake_case__ = ("UpDecoderBlock2D",)  ,     snake_case__ = (64,)  ,     snake_case__ = 1  ,     snake_case__ = "silu"  ,     snake_case__ = 3  ,     snake_case__ = 32  ,     snake_case__ = 256  ,     snake_case__ = 32  ,     snake_case__ = None  ,     snake_case__ = 0.18_215  ,     snake_case__ = "group"  ,     ):




       """simple docstring"""


       super().__init__()

       # pass init params to Encoder
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Encoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     down_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     double_z=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Dict							    = vq_embed_dim if vq_embed_dim is not None else latent_channels

       _SCREAMING_SNAKE_CASE   :				Tuple							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)

       # pass init params to Decoder
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Decoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     up_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     norm_type=snake_case__  ,     )
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.encoder(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.quant_conv(snake_case__				)

       if not return_dict:
         return (h,)

       return VQEncoderOutput(latents=snake_case__				)
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = True				):




       """simple docstring"""


       if not force_not_quantize:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = self.quantize(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				int							    = h
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.post_quant_conv(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.decoder(snake_case__  ,     quant if self.config.norm_type == "spatial" else None				)

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = sample
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.encode(snake_case__				).latents
       _SCREAMING_SNAKE_CASE   :				Any							    = self.decode(snake_case__				).sample

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)



