





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case								=							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						Union[str, Any]    ,__A						:						Union[str, Any]    ,__A						:						Optional[Any]=768	)    ->       Optional[int]:
         super().__init__(__A	)
         _lowercase						    =			proj_size

         _lowercase						    =			CLIPVisionModel(__A	)
         _lowercase						    =			PaintByExampleMapper(__A	)
         _lowercase						    =			nn.LayerNorm(config.hidden_size	)
         _lowercase						    =			nn.Linear(config.hidden_size    ,self.proj_size	)

         # uncondition for scaling
         _lowercase						    =			nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						Optional[int]    ,__A						:						Optional[int]=False	)    ->       Union[str, Any]:
         _lowercase						    =			self.model(pixel_values=__A	)
         _lowercase						    =			clip_output.pooler_output
         _lowercase						    =			self.mapper(latent_states[:, None]	)
         _lowercase						    =			self.final_layer_norm(__A	)
         _lowercase						    =			self.proj_out(__A	)
         if return_uncond_vector:
           return latent_states, self.uncond_vector

         return latent_states





class 					A_					(   nn.Module						):







       """simple docstring"""





       def __init__(     self						:						Optional[int]    ,__A						:						Dict	)    ->       str:
         super().__init__()
         _lowercase						    =			(config.num_hidden_layers + 1) // 5
         _lowercase						    =			config.hidden_size
         _lowercase						    =			1
         _lowercase						    =			nn.ModuleList(
             [
                 BasicTransformerBlock(__A    ,__A    ,__A    ,activation_fn='gelu'    ,attention_bias=__A	)
                 for _ in range(__A	)
             ]	)







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						Optional[Any]	)    ->       Dict:
         for block in self.blocks:
           _lowercase						    =			block(__A	)

         return hidden_states



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


snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

          lowerCamelCase_						:   str		= vocab_size
          lowerCamelCase_						:   List[Any]		= hidden_size
          lowerCamelCase_						:   Optional[int]		= num_hidden_layers
          lowerCamelCase_						:   Optional[Any]		= num_attention_heads
          lowerCamelCase_						:   List[Any]		= hidden_act
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   List[Any]		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= type_vocab_size
          lowerCamelCase_						:   Dict		= initializer_range
          lowerCamelCase_						:   List[Any]		= layer_norm_eps
          lowerCamelCase_						:   List[str]		= projection_dim
          lowerCamelCase_						:   Optional[int]		= position_embedding_type





