


'''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'''




 __lowerCAmelCase						: Optional[torch.FloatTensor]							  =		None
 __lowerCAmelCase						: torch.FloatTensor							  =		None
 __lowerCAmelCase						: Optional[Tuple[torch.FloatTensor]]							  =		None
 __lowerCAmelCase						: Optional[Tuple[torch.FloatTensor]]							  =		None







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=2		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_="cls"		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      **lowerCamelCase_		,      )      ->    int:
    super().__init__(pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_		,      **lowerCamelCase_      )
    _a					:  int						=  project_dim
    _a					:  List[Any]						=  pooler_fn
    _a					:  int						=  learn_encoder
    _a					:  Optional[Any]						=  use_attention_mask







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Union[str, Any]							  =		[r"""pooler""", r"""logit_scale"""]
 __lowerCAmelCase						: Any							  =		[r"""position_ids""", r"""predictions.decoder.bias"""]
 __lowerCAmelCase						: List[Any]							  =		"""roberta"""
 __lowerCAmelCase						: int							  =		RobertaSeriesConfig




 def __init__(    self		,      lowerCamelCase_      )      ->    Dict:
    super().__init__(lowerCamelCase_      )
    _a					:  int						=  XLMRobertaModel(lowerCamelCase_      )
    _a					:  Tuple						=  nn.Linear(config.hidden_size		,      config.project_dim      )
    _a					:  Any						=  getattr(lowerCamelCase_		,      'has_pre_transformation'		,      lowerCamelCase_      )
    if self.has_pre_transformation:
       _a					:  Optional[Any]						=  nn.Linear(config.hidden_size		,      config.project_dim      )
       _a					:  Tuple						=  nn.LayerNorm(config.hidden_size		,      eps=config.layer_norm_eps      )
    self.post_init()




 def   __UpperCamelCase   (    self		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      )      ->    Tuple:
    _a					:  int						=  return_dict if return_dict is not None else self.config.use_return_dict

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




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    None:
    warnings.warn(
        'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
        ' Please use PerceiverImageProcessor instead.'		,      lowerCamelCase_		,      )
    super().__init__(*lowerCamelCase_		,      **lowerCamelCase_      )


