
"""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     lowercase(_lowercase       ):
     __snake_case:	torch.FloatTensor







class     lowercase(_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE = 3     ,				__SCREAMING_SNAKE_CASE = 3     ,				__SCREAMING_SNAKE_CASE = ("DownEncoderBlock2D",)     ,				__SCREAMING_SNAKE_CASE = ("UpDecoderBlock2D",)     ,				__SCREAMING_SNAKE_CASE = (6_4,)     ,				__SCREAMING_SNAKE_CASE = 1     ,				__SCREAMING_SNAKE_CASE = "silu"     ,				__SCREAMING_SNAKE_CASE = 3     ,				__SCREAMING_SNAKE_CASE = 3_2     ,				__SCREAMING_SNAKE_CASE = 2_5_6     ,				__SCREAMING_SNAKE_CASE = 3_2     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = 0.1_82_15     ,				__SCREAMING_SNAKE_CASE = "group"     ,				)		->     Any:







         """simple docstring"""



         super().__init__()

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

         a__		=			vq_embed_dim if vq_embed_dim is not None else latent_channels

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

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



     @apply_forward_hook
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = True   )		->     VQEncoderOutput:







         """simple docstring"""



         a__		=			self.encoder(__SCREAMING_SNAKE_CASE   )
         a__		=			self.quant_conv(__SCREAMING_SNAKE_CASE   )

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=__SCREAMING_SNAKE_CASE   )



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







         """simple docstring"""



         if not force_not_quantize:
             a__	,     a__	,     a__		=			self.quantize(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			h
         a__		=			self.post_quant_conv(__SCREAMING_SNAKE_CASE   )
         a__		=			self.decoder(__SCREAMING_SNAKE_CASE     ,				quant if self.config.norm_type == 'spatial' else None   )

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=__SCREAMING_SNAKE_CASE   )




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







         """simple docstring"""



         a__		=			sample
         a__		=			self.encode(__SCREAMING_SNAKE_CASE   ).latents
         a__		=			self.decode(__SCREAMING_SNAKE_CASE   ).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=__SCREAMING_SNAKE_CASE   )




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


