"""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 						__A							(			a__		):
     _UpperCamelCase							:      Optional[Any]		 =		42







class 						__A							(			a__   ,     a__		):






     @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_8_2_1_5    ,					a__ = "group"    ,					):
       super().__init__()

       # pass init params to Encoder
       _lowerCAmelCase						:	Optional[Any]        =					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    ,					)

       _lowerCAmelCase						:	int        =					vq_embed_dim if vq_embed_dim is not None else latent_channels

       _lowerCAmelCase						:	Tuple        =					nn.Convad(_lowerCamelCase    ,					_lowerCamelCase    ,					1  )
       _lowerCAmelCase						:	Dict        =					VectorQuantizer(_lowerCamelCase    ,					_lowerCamelCase    ,					beta=0.2_5    ,					remap=_lowerCamelCase    ,					sane_index_shape=_lowerCamelCase  )
       _lowerCAmelCase						:	str        =					nn.Convad(_lowerCamelCase    ,					_lowerCamelCase    ,					1  )

       # pass init params to Decoder
       _lowerCAmelCase						:	List[Any]        =					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    __A  (		self    ,					a__    ,					a__ = True  ):
       _lowerCAmelCase						:	Any        =					self.encoder(_lowerCamelCase  )
       _lowerCAmelCase						:	str        =					self.quant_conv(_lowerCamelCase  )

       if not return_dict:
         return (h,)

       return VQEncoderOutput(latents=_lowerCamelCase  )






     @apply_forward_hook
     def    __A  (		self    ,					a__    ,					a__ = False    ,					a__ = True  ):







       if not force_not_quantize:
         _lowerCAmelCase						:	Union[str, Any]        =					self.quantize(_lowerCamelCase  )
       else:
         _lowerCAmelCase						:	Tuple        =					h
       _lowerCAmelCase						:	int        =					self.post_quant_conv(_lowerCamelCase  )
       _lowerCAmelCase						:	List[Any]        =					self.decoder(_lowerCamelCase    ,					quant if self.config.norm_type == """spatial""" else None  )

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=_lowerCamelCase  )






     def    __A  (		self    ,					a__    ,					a__ = True  ):
       _lowerCAmelCase						:	List[str]        =					sample
       _lowerCAmelCase						:	Dict        =					self.encode(_lowerCamelCase  ).latents
       _lowerCAmelCase						:	Optional[Any]        =					self.decode(_lowerCamelCase  ).sample

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=_lowerCamelCase  )





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




