





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    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      def __init__(		self:			List[Any]	,    __lowerCamelCase:			Union[str, Any]=1	,    __lowerCamelCase:			str=0	,    __lowerCamelCase:			Optional[Any]=2	,    __lowerCamelCase:			List[str]=512	,    __lowerCamelCase:			Union[str, Any]="cls"	,    __lowerCamelCase:			Any=False	,    __lowerCamelCase:			Optional[Any]=True	,    **__lowerCamelCase:			Optional[Any]	,    ):
          '''simple docstring'''


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




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       [r"""pooler""", r"""logit_scale"""]
      UpperCamelCase__  		=       [r"""position_ids""", r"""predictions.decoder.bias"""]
      UpperCamelCase__  		=       """roberta"""
      UpperCamelCase__  		=       RobertaSeriesConfig






      def __init__(		self:			Dict	,    __lowerCamelCase:			str					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    __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	,    ):
          '''simple docstring'''


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

          UpperCamelCase__:	Tuple					=     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[Any]					=     outputs["hidden_states"][-2]
              UpperCamelCase__:	Union[str, Any]					=     self.pre_LN(__lowerCamelCase					)
              UpperCamelCase__:	int					=     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__:	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	,    )








def 				lowerCAmelCase_ (							A_):
    if not all(char in "01" for char in bin_string):
        raise ValueError("Non-binary value was passed to the function")
    if not bin_string:
        raise ValueError("Empty string was passed to the function")
    UpperCamelCase__:	List[Any]					=     ""
    while len(A_) % 3 != 0:
        UpperCamelCase__:	int					=     "0" + bin_string
    UpperCamelCase__:	Optional[int]					=     [
        bin_string[index : index + 3]
        for index in range(len(A_))
        if index % 3 == 0
    ]
    for bin_group in bin_string_in_3_list:
        UpperCamelCase__:	Union[str, Any]					=     0
        for index, val in enumerate(A_):
            oct_val += int(2 ** (2 - index) * int(A_))
        oct_string += str(A_)
    return oct_string


if __name__ == "__main__":
   from doctest import testmod

   testmod()

