


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       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[torch.FloatTensor]			=      None
     A:		torch.FloatTensor			=      None
     A:		Optional[Tuple[torch.FloatTensor]]			=      None
     A:		Optional[Tuple[torch.FloatTensor]]			=      None



class       __magic_name__	(     __lowerCAmelCase):


     def __init__(      self							:     int							,	lowerCamelCase__							:     Tuple=1							,	lowerCamelCase__							:     Dict=0							,	lowerCamelCase__							:     Union[str, Any]=2							,	lowerCamelCase__							:     Optional[Any]=512							,	lowerCamelCase__							:     Optional[Any]="cls"							,	lowerCamelCase__							:     Optional[int]=False							,	lowerCamelCase__							:     Any=True							,	**lowerCamelCase__							:     List[str]							,	)  ->      Tuple:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   project_dim
      UpperCamelCase__					:    Union[str, Any]          =   pooler_fn
      UpperCamelCase__					:    Optional[int]          =   learn_encoder
      UpperCamelCase__					:    str          =   use_attention_mask



class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      [r"pooler", r"logit_scale"]
     A:		Dict			=      [r"position_ids", r"predictions.decoder.bias"]
     A:		Any			=      "roberta"
     A:		Tuple			=      RobertaSeriesConfig


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      super().__init__(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   XLMRobertaModel(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   nn.Linear(config.hidden_size							,	config.project_dim			)
      UpperCamelCase__					:    Dict          =   getattr(lowerCamelCase__							,	'''has_pre_transformation'''							,	lowerCamelCase__			)
      if self.has_pre_transformation:
       UpperCamelCase__					:    Dict          =   nn.Linear(config.hidden_size							,	config.project_dim			)
       UpperCamelCase__					:    Union[str, Any]          =   nn.LayerNorm(config.hidden_size							,	eps=config.layer_norm_eps			)
      self.post_init()


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   return_dict if return_dict is not None else self.config.use_return_dict

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

      if self.has_pre_transformation:
       UpperCamelCase__					:    Optional[int]          =   outputs['''hidden_states'''][-2]
       UpperCamelCase__					:    List[Any]          =   self.pre_LN(lowerCamelCase__			)
       UpperCamelCase__					:    Dict          =   self.transformation_pre(lowerCamelCase__			)

       return TransformationModelOutput(
           projection_state=lowerCamelCase__							,	last_hidden_state=outputs.last_hidden_state							,	hidden_states=outputs.hidden_states							,	attentions=outputs.attentions							,	)
      else:
       UpperCamelCase__					:    Optional[Any]          =   self.transformation(outputs.last_hidden_state			)
       return TransformationModelOutput(
           projection_state=lowerCamelCase__							,	last_hidden_state=outputs.last_hidden_state							,	hidden_states=outputs.hidden_states							,	attentions=outputs.attentions							,	)






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__UpperCamelCase       :       int						       =				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       __UpperCamelCase       :       List[str]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


