




'''simple docstring'''






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

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


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






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Optional[Any]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple=768			):
           super().__init__(__lowerCamelCase			)
           snake_case__       :   str	      =    proj_size

           snake_case__       :   List[str]	      =    CLIPVisionModel(__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    PaintByExampleMapper(__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    nn.LayerNorm(config.hidden_size			)
           snake_case__       :   int	      =    nn.Linear(config.hidden_size     ,							self.proj_size			)

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





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[int]=False			):
           snake_case__       :   Optional[int]	      =    self.model(pixel_values=__lowerCamelCase			)
           snake_case__       :   int	      =    clip_output.pooler_output
           snake_case__       :   List[Any]	      =    self.mapper(latent_states[:, None]			)
           snake_case__       :   List[Any]	      =    self.final_layer_norm(__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    self.proj_out(__lowerCamelCase			)
           if return_uncond_vector:
                 return latent_states, self.uncond_vector

           return latent_states






class        lowercase_      (						nn.Module							):





     def __init__(     self     :					List[Any]     ,							__lowerCamelCase     :					str			):
           super().__init__()
           snake_case__       :   Any	      =    (config.num_hidden_layers + 1) // 5
           snake_case__       :   str	      =    config.hidden_size
           snake_case__       :   str	      =    1
           snake_case__       :   int	      =    nn.ModuleList(
               [
                   BasicTransformerBlock(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							activation_fn='gelu'     ,							attention_bias=__lowerCamelCase			)
                   for _ in range(__lowerCamelCase			)
               ]			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					Optional[Any]			):
           for block in self.blocks:
                 snake_case__       :   str	      =    block(__lowerCamelCase			)

           return hidden_states






'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
