


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

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


snake_case				:			Union[str, Any]	     = logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			snake_case_  (A__ ):
					def __init__( self  :List[Any]     ,__snake_case  :Optional[Any]     ,__snake_case  :Optional[int]=7_68 )   ->       str:
												super().__init__(_UpperCamelCase )
												a__			 =	proj_size

												a__			 =	CLIPVisionModel(_UpperCamelCase )
												a__			 =	PaintByExampleMapper(_UpperCamelCase )
												a__			 =	nn.LayerNorm(config.hidden_size )
												a__			 =	nn.Linear(config.hidden_size     ,self.proj_size )

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



					def 					lowerCamelCase__( self  :int     ,__snake_case  :Dict     ,__snake_case  :Dict=False )   ->       Any:
												a__			 =	self.model(pixel_values=_UpperCamelCase )
												a__			 =	clip_output.pooler_output
												a__			 =	self.mapper(latent_states[:, None] )
												a__			 =	self.final_layer_norm(_UpperCamelCase )
												a__			 =	self.proj_out(_UpperCamelCase )
												if return_uncond_vector:
																			return latent_states, self.uncond_vector

												return latent_states







class 			snake_case_  (nn.Module ):
					def __init__( self  :Tuple     ,__snake_case  :str )   ->       Optional[Any]:
												super().__init__()
												a__			 =	(config.num_hidden_layers + 1) // 5
												a__			 =	config.hidden_size
												a__			 =	1
												a__			 =	nn.ModuleList(
												    [
												        BasicTransformerBlock(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,activation_fn='gelu'     ,attention_bias=_UpperCamelCase )
												        for _ in range(_UpperCamelCase )
												    ] )



					def 					lowerCamelCase__( self  :Dict     ,__snake_case  :Optional[int] )   ->       Optional[int]:
												for block in self.blocks:
																			a__			 =	block(_UpperCamelCase )

												return hidden_states











def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()