
"""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 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_=1		,			UpperCAmelCase_=0		,			UpperCAmelCase_=2		,			UpperCAmelCase_=512		,			UpperCAmelCase_="cls"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							List[Any]:
			super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_ )
			lowerCamelCase					:      str          =   project_dim
			lowerCamelCase					:      Dict          =   pooler_fn
			lowerCamelCase					:      Any          =   learn_encoder
			lowerCamelCase					:      Optional[int]          =   use_attention_mask






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     [r'pooler', r'logit_scale']
	lowercase_				=     [r'position_ids', r'predictions.decoder.bias']
	lowercase_				=     'roberta'
	lowercase_				=     RobertaSeriesConfig




	def __init__(			self		,			UpperCAmelCase_ )		->							Union[str, Any]:
			super().__init__(lowercase_ )
			lowerCamelCase					:      Tuple          =   XLMRobertaModel(lowercase_ )
			lowerCamelCase					:      Tuple          =   nn.Linear(config.hidden_size		,			config.project_dim )
			lowerCamelCase					:      str          =   getattr(lowercase_		,			'has_pre_transformation'		,			lowercase_ )
			if self.has_pre_transformation:
					lowerCamelCase					:      Optional[Any]          =   nn.Linear(config.hidden_size		,			config.project_dim )
					lowerCamelCase					:      Tuple          =   nn.LayerNorm(config.hidden_size		,			eps=config.layer_norm_eps )
			self.post_init()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			)		->							str:
			lowerCamelCase					:      Tuple          =   return_dict if return_dict is not None else self.config.use_return_dict

			lowerCamelCase					:      Union[str, Any]          =   self.base_model(
			    input_ids=lowercase_		,			attention_mask=lowercase_		,			token_type_ids=lowercase_		,			position_ids=lowercase_		,			head_mask=lowercase_		,			inputs_embeds=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			output_attentions=lowercase_		,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,			return_dict=lowercase_		,			)

			if self.has_pre_transformation:
					lowerCamelCase					:      Dict          =   outputs['hidden_states'][-2]
					lowerCamelCase					:      Tuple          =   self.pre_LN(lowercase_ )
					lowerCamelCase					:      Union[str, Any]          =   self.transformation_pre(lowercase_ )

					return TransformationModelOutput(
					    projection_state=lowercase_		,			last_hidden_state=outputs.last_hidden_state		,			hidden_states=outputs.hidden_states		,			attentions=outputs.attentions		,			)
			else:
					lowerCamelCase					:      Optional[int]          =   self.transformation(outputs.last_hidden_state )
					return TransformationModelOutput(
					    projection_state=lowercase_		,			last_hidden_state=outputs.last_hidden_state		,			hidden_states=outputs.hidden_states		,			attentions=outputs.attentions		,			)

"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






