


'''simple docstring'''



from ..utils import DummyObject, requires_backends





class 							__UpperCamelCase    (		metaclass=lowerCAmelCase_ ):
	A_           =   ["transformers", "torch", "note_seq"]




	def __init__(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						requires_backends(self						,    ['transformers', 'torch', 'note_seq']							)




	@classmethod
	def 				__UpperCAmelCase							(       cls						,    *__a						,    **__a							):

						'''simple docstring'''





						requires_backends(cls						,    ['transformers', 'torch', 'note_seq']							)





	@classmethod
	def 				__UpperCAmelCase							(       cls						,    *__a						,    **__a							):

						'''simple docstring'''





						requires_backends(cls						,    ['transformers', 'torch', 'note_seq']							)







'''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_ ):
	A_           =   None
	A_           =   None
	A_           =   None
	A_           =   None





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a=1						,    __a=0						,    __a=2						,    __a=512						,    __a="cls"						,    __a=False						,    __a=True						,    **__a						,    ):

						'''simple docstring'''





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





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   [r"pooler", r"logit_scale"]
	A_           =   [r"position_ids", r"predictions.decoder.bias"]
	A_           =   "roberta"
	A_           =   RobertaSeriesConfig




	def __init__(       self						,    __a							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Tuple    =  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      :	Optional[Any]    =  outputs['hidden_states'][-2]
											__a      :	Optional[int]    =  self.pre_LN(__a							)
											__a      :	Union[str, Any]    =  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      :	Optional[Any]    =  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						,    )



