


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        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[str]          =			None
					UpperCAmelCase__					:    Tuple          =			None
					UpperCAmelCase__					:    Dict          =			None
					UpperCAmelCase__					:    int          =			None







class        snake_case_   (lowerCamelCase_      ):
					def __init__(      self   :Tuple			,__snake_case   :Tuple=1			,__snake_case   :List[Any]=0			,__snake_case   :Any=2			,__snake_case   :Any=5_12			,__snake_case   :Optional[Any]="cls"			,__snake_case   :List[Any]=False			,__snake_case   :Tuple=True			,**__snake_case   :Tuple			,)					->    int:
												super().__init__(pad_token_id=__a			,bos_token_id=__a			,eos_token_id=__a			,**__a      )
												a__             =     project_dim
												a__             =     pooler_fn
												a__             =     learn_encoder
												a__             =     use_attention_mask







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Union[str, Any]          =			[R'''pooler''', R'''logit_scale''']
					UpperCAmelCase__					:    List[Any]          =			[R'''position_ids''', R'''predictions.decoder.bias''']
					UpperCAmelCase__					:    Any          =			'''roberta'''
					UpperCAmelCase__					:    Optional[int]          =			RobertaSeriesConfig
					def __init__(      self   :Optional[int]			,__snake_case   :List[Any]      )					->    List[Any]:
												super().__init__(__a      )
												a__             =     XLMRobertaModel(__a      )
												a__             =     nn.Linear(config.hidden_size			,config.project_dim      )
												a__             =     getattr(__a			,'has_pre_transformation'			,__a      )
												if self.has_pre_transformation:
																			a__             =     nn.Linear(config.hidden_size			,config.project_dim      )
																			a__             =     nn.LayerNorm(config.hidden_size			,eps=config.layer_norm_eps      )
												self.post_init()



					def  lowerCamelCase__(      self   :Any			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[torch.Tensor] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[bool] = None			,)					->    List[str]:
												a__             =     return_dict if return_dict is not None else self.config.use_return_dict

												a__             =     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:
																			a__             =     outputs['hidden_states'][-2]
																			a__             =     self.pre_LN(__a      )
																			a__             =     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:
																			a__             =     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			,)











'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      bytes						)	->	str:
				return "".join([hex(lowercase						)[2:].zfill(2						).upper() for byte in list(lowercase						)]						)






def  _lowerCamelCase				( lowercase						:      str						)	->	bytes:
				# Check data validity, following RFC3548
				# https://www.ietf.org/rfc/rfc3548.txt
				if (len(lowercase						) % 2) != 0:
								raise ValueError(
								    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."						)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(lowercase						) <= set("0123456789ABCDEF"						):
								raise ValueError(
								    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."						)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1]    ,							16						) for i in range(0    ,							len(lowercase						)    ,							2						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
