
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       None
							__lowercase      		=       None
							__lowercase      		=       None
							__lowercase      		=       None


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=0    ,   lowerCAmelCase_=2    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_="cls"    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )
								_snake_case   						=		project_dim
								_snake_case   						=		pooler_fn
								_snake_case   						=		learn_encoder
								_snake_case   						=		use_attention_mask


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       [r"""pooler""", r"""logit_scale"""]
							__lowercase      		=       [r"""position_ids""", r"""predictions.decoder.bias"""]
							__lowercase      		=       """roberta"""
							__lowercase      		=       RobertaSeriesConfig


							def __init__(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(lowerCAmelCase_  )
								_snake_case   						=		XLMRobertaModel(lowerCAmelCase_  )
								_snake_case   						=		nn.Linear(config.hidden_size    ,   config.project_dim  )
								_snake_case   						=		getattr(lowerCAmelCase_    ,   'has_pre_transformation'    ,   lowerCAmelCase_  )
								if self.has_pre_transformation:
									_snake_case   						=		nn.Linear(config.hidden_size    ,   config.project_dim  )
									_snake_case   						=		nn.LayerNorm(config.hidden_size    ,   eps=config.layer_norm_eps  )
								self.post_init()




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   ):





								"""simple docstring"""






								_snake_case   						=		return_dict if return_dict is not None else self.config.use_return_dict

								_snake_case   						=		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:
									_snake_case   						=		outputs['hidden_states'][-2]
									_snake_case   						=		self.pre_LN(lowerCAmelCase_  )
									_snake_case   						=		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:
									_snake_case   						=		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    ,   )



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	bool:
	return number & 1 == 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()

