


'''simple docstring'''





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

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


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





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def __init__(	self				:   Tuple       ,   __snake_case				:   Dict       ,   __snake_case				:   int=768	):





       '''simple docstring'''
       super().__init__(__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   proj_size

       UpperCAmelCase_							:      Tuple						=   CLIPVisionModel(__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   PaintByExampleMapper(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   nn.LayerNorm(config.hidden_size	)
       UpperCAmelCase_							:      Optional[Any]						=   nn.Linear(config.hidden_size       ,   self.proj_size	)

       # uncondition for scaling
       UpperCAmelCase_							:      List[Any]						=   nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   str=False	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.model(pixel_values=__snake_case	)
       UpperCAmelCase_							:      int						=   clip_output.pooler_output
       UpperCAmelCase_							:      Optional[Any]						=   self.mapper(latent_states[:, None]	)
       UpperCAmelCase_							:      List[str]						=   self.final_layer_norm(__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   self.proj_out(__snake_case	)
       if return_uncond_vector:
          return latent_states, self.uncond_vector

       return latent_states





class  lowerCAmelCase__(    nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   int       ,   __snake_case				:   Dict	):





       '''simple docstring'''
       super().__init__()
       UpperCAmelCase_							:      List[Any]						=   (config.num_hidden_layers + 1) // 5
       UpperCAmelCase_							:      str						=   config.hidden_size
       UpperCAmelCase_							:      Union[str, Any]						=   1
       UpperCAmelCase_							:      int						=   nn.ModuleList(
           [
               BasicTransformerBlock(__snake_case       ,   __snake_case       ,   __snake_case       ,   activation_fn='''gelu'''       ,   attention_bias=__snake_case	)
               for _ in range(__snake_case	)
           ]	)





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       for block in self.blocks:
          UpperCAmelCase_							:      Tuple						=   block(__snake_case	)

       return hidden_states



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))