





"""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_							):







     '''simple docstring'''




     def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=768       ):
         super().__init__(__SCREAMING_SNAKE_CASE       )
         __a	:  List[Any]         =					proj_size

         __a	:  Dict         =					CLIPVisionModel(__SCREAMING_SNAKE_CASE       )
         __a	:  Union[str, Any]         =					PaintByExampleMapper(__SCREAMING_SNAKE_CASE       )
         __a	:  Dict         =					nn.LayerNorm(config.hidden_size       )
         __a	:  int         =					nn.Linear(config.hidden_size		,     self.proj_size       )

         # uncondition for scaling
         __a	:  Optional[int]         =					nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=False       ):
         __a	:  Union[str, Any]         =					self.model(pixel_values=__SCREAMING_SNAKE_CASE       )
         __a	:  Dict         =					clip_output.pooler_output
         __a	:  Optional[Any]         =					self.mapper(latent_states[:, None]       )
         __a	:  Any         =					self.final_layer_norm(__SCREAMING_SNAKE_CASE       )
         __a	:  List[str]         =					self.proj_out(__SCREAMING_SNAKE_CASE       )
         if return_uncond_vector:
             return latent_states, self.uncond_vector

         return latent_states






class 			__lowercase     (     nn.Module							):







     '''simple docstring'''




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







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):

         for block in self.blocks:
             __a	:  Union[str, Any]         =					block(__SCREAMING_SNAKE_CASE       )

         return hidden_states






"""simple docstring"""






def       __A (     a_  :int				,    a_  :int)						->						str:
				if a < 0 or b < 0:
								raise ValueError('''the value of both inputs must be positive''')

				__a	:  Union[str, Any]         =					str(bin(a_))[2:]  # remove the leading "0b"
				__a	:  Union[str, Any]         =					str(bin(a_))[2:]  # remove the leading "0b"

				__a	:  Optional[Any]         =					max(len(a_)				,    len(a_))

				return "0b" + "".join(
				    str(int(char_a != char_b))
				    for char_a, char_b in zip(a_binary.zfill(a_)				,    b_binary.zfill(a_)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()