






class        a       :



      """simple docstring"""


      def __init__( self   :     Optional[int]						, __lowercase   :     int   )				-> None:
        __UpperCAmelCase				:      Any	 =    size
        __UpperCAmelCase				:      Dict	 =    [0] * size
        __UpperCAmelCase				:      Union[str, Any]	 =    [0] * size
      @staticmethod
      def       UpperCAmelCase ( __lowercase   :     int   )				-> int:
        return index | (index + 1)
      @staticmethod
      def       UpperCAmelCase ( __lowercase   :     int   )				-> int:
        return (index & (index + 1)) - 1
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     int						, __lowercase   :     int   )				-> None:
        __UpperCAmelCase				:      Dict	 =    value
        while index < self.size:
          __UpperCAmelCase				:      Optional[Any]	 =    self.get_prev(__lowercase   ) + 1
          if current_left_border == index:
            __UpperCAmelCase				:      List[Any]	 =    value
          else:
            __UpperCAmelCase				:      List[str]	 =    max(__lowercase						, __lowercase						, __lowercase   )
          __UpperCAmelCase				:      Any	 =    self.get_next(__lowercase   )




      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     int						, __lowercase   :     int   )				-> int:
        right -= 1  # Because of right is exclusive
        __UpperCAmelCase				:      str	 =    0
        while left <= right:
          __UpperCAmelCase				:      str	 =    self.get_prev(__lowercase   )
          if left <= current_left:
            __UpperCAmelCase				:      str	 =    max(__lowercase						, self.tree[right]   )
            __UpperCAmelCase				:      Optional[Any]	 =    current_left
          else:
            __UpperCAmelCase				:      List[Any]	 =    max(__lowercase						, self.arr[right]   )
            right -= 1
        return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()








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

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


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

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



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

