






'''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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=None
      __a  					=None
      __a  					=None
      __a  					=None







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase=1 ,   lowerCamelCase=0 ,   lowerCamelCase=2 ,   lowerCamelCase=512 ,   lowerCamelCase="cls" ,   lowerCamelCase=False ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->str:



         '''simple docstring'''
         super().__init__(pad_token_id=lowerCamelCase ,   bos_token_id=lowerCamelCase ,   eos_token_id=lowerCamelCase ,   **lowerCamelCase      )
         __a				     =    project_dim
         __a				     =    pooler_fn
         __a				     =    learn_encoder
         __a				     =    use_attention_mask







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[R"pooler", R"logit_scale"]
      __a  					=[R"position_ids", R"predictions.decoder.bias"]
      __a  					="roberta"
      __a  					=RobertaSeriesConfig




      def __init__(  self ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         super().__init__(lowerCamelCase      )
         __a				     =    XLMRobertaModel(lowerCamelCase      )
         __a				     =    nn.Linear(config.hidden_size ,   config.project_dim      )
         __a				     =    getattr(lowerCamelCase ,   'has_pre_transformation' ,   lowerCamelCase      )
         if self.has_pre_transformation:
            __a				     =    nn.Linear(config.hidden_size ,   config.project_dim      )
            __a				     =    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 ,   )							->Any:



         '''simple docstring'''
         __a				     =    return_dict if return_dict is not None else self.config.use_return_dict

         __a				     =    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				     =    outputs['hidden_states'][-2]
            __a				     =    self.pre_LN(lowerCamelCase      )
            __a				     =    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				     =    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'''



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


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias