




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_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= None
	A    							= None
	A    							= None
	A    							= None




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=0      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=512      ,     SCREAMING_SNAKE_CASE="cls"      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,     bos_token_id=SCREAMING_SNAKE_CASE      ,     eos_token_id=SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						project_dim
				SCREAMING_SNAKE_CASE_								=						pooler_fn
				SCREAMING_SNAKE_CASE_								=						learn_encoder
				SCREAMING_SNAKE_CASE_								=						use_attention_mask




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= [r'''pooler''', r'''logit_scale''']
	A    							= [r'''position_ids''', r'''predictions.decoder.bias''']
	A    							= '''roberta'''
	A    							= RobertaSeriesConfig





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				super().__init__(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						XLMRobertaModel(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						nn.Linear(config.hidden_size      ,     config.project_dim      )
				SCREAMING_SNAKE_CASE_								=						getattr(SCREAMING_SNAKE_CASE      ,     'has_pre_transformation'      ,     SCREAMING_SNAKE_CASE      )
				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 						A_(							self      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					Tuple:




				"""simple docstring"""



				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=SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     token_type_ids=SCREAMING_SNAKE_CASE      ,     position_ids=SCREAMING_SNAKE_CASE      ,     head_mask=SCREAMING_SNAKE_CASE      ,     inputs_embeds=SCREAMING_SNAKE_CASE      ,     encoder_hidden_states=SCREAMING_SNAKE_CASE      ,     encoder_attention_mask=SCREAMING_SNAKE_CASE      ,     output_attentions=SCREAMING_SNAKE_CASE      ,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states      ,     return_dict=SCREAMING_SNAKE_CASE      ,     )

				if self.has_pre_transformation:
							SCREAMING_SNAKE_CASE_								=						outputs['hidden_states'][-2]
							SCREAMING_SNAKE_CASE_								=						self.pre_LN(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						self.transformation_pre(SCREAMING_SNAKE_CASE      )

							return TransformationModelOutput(
							    projection_state=SCREAMING_SNAKE_CASE      ,     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=SCREAMING_SNAKE_CASE      ,     last_hidden_state=outputs.last_hidden_state      ,     hidden_states=outputs.hidden_states      ,     attentions=outputs.attentions      ,     )






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
				import accelerate

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if not is_accelerate_available():
						return method
			SCREAMING_SNAKE_CASE_								=						version.parse(accelerate.__version__ ).base_version
			if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('0.17.0' ):
						return method

			def wrapper(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE ):
						if hasattr(self       ,    '_hf_hook' ) and hasattr(self._hf_hook       ,    'pre_forward' ):
									self._hf_hook.pre_forward(self )
						return method(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE )

			return wrapper
