





'''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_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = None
    UpperCamelCase     = None
    UpperCamelCase     = None
    UpperCamelCase     = None




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A=1 ,		A=0 ,		A=2 ,		A=512 ,		A="cls" ,		A=False ,		A=True ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )
        _SCREAMING_SNAKE_CASE									=			project_dim
        _SCREAMING_SNAKE_CASE									=			pooler_fn
        _SCREAMING_SNAKE_CASE									=			learn_encoder
        _SCREAMING_SNAKE_CASE									=			use_attention_mask




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = [R'''pooler''', R'''logit_scale''']
    UpperCamelCase     = [R'''position_ids''', R'''predictions.decoder.bias''']
    UpperCamelCase     = '''roberta'''
    UpperCamelCase     = RobertaSeriesConfig


    def __init__(		self ,		A       )		->   Optional[int]:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			XLMRobertaModel(A       )
        _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.project_dim       )
        _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""has_pre_transformation""" ,		A       )
        if self.has_pre_transformation:
            _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.project_dim       )
            _SCREAMING_SNAKE_CASE									=			nn.LayerNorm(config.hidden_size ,		eps=config.layer_norm_eps       )
        self.post_init()





    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		)		->   Any:
        _SCREAMING_SNAKE_CASE									=			return_dict if return_dict is not None else self.config.use_return_dict

        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			outputs["""hidden_states"""][-2]
            _SCREAMING_SNAKE_CASE									=			self.pre_LN(A       )
            _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			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'''

from string import ascii_lowercase, ascii_uppercase





def lowerCamelCase						(     __lowerCamelCase :    str    )						->str:
    if not sentence:
        return ""
    _SCREAMING_SNAKE_CASE									=			dict(zip(__lowerCamelCase					,       __lowerCamelCase    )    )
    return lower_to_upper.get(sentence[0]					,       sentence[0]    ) + sentence[1:]


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

      testmod()



