






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

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


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




class     _lowerCAmelCase(	UpperCAmelCase_ ):





							"""simple docstring"""







							def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=7_6_8							):
									super().__init__(__lowercase							)
									UpperCamelCase_: str   				=    proj_size

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

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





							def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=False							):
									UpperCamelCase_: Optional[Any]   				=    self.model(pixel_values=__lowercase							)
									UpperCamelCase_: Optional[int]   				=    clip_output.pooler_output
									UpperCamelCase_: int   				=    self.mapper(latent_states[:, None]							)
									UpperCamelCase_: Tuple   				=    self.final_layer_norm(__lowercase							)
									UpperCamelCase_: Union[str, Any]   				=    self.proj_out(__lowercase							)
									if return_uncond_vector:
											return latent_states, self.uncond_vector

									return latent_states







class     _lowerCAmelCase(	nn.Module ):





							"""simple docstring"""







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





							def       _a   ( self  ,      _lowerCamelCase							):
									for block in self.blocks:
											UpperCamelCase_: Optional[Any]   				=    block(__lowercase							)

									return hidden_states







def   snake_case (UpperCAmelCase__  )	->    int:
  assert isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  ), F'''The input value of [n={number}] is not an integer'''

  if number == 1:
    return 2
  elif number < 1:
    UpperCamelCase_: List[Any]   				=    F'''The input value of [n={number}] has to be > 0'''
    raise ValueError(UpperCAmelCase__  )
  else:
    UpperCamelCase_: str   				=    sylvester(number - 1  )
    UpperCamelCase_: str   				=    num - 1
    UpperCamelCase_: Any   				=    num
    return lower * upper + 1


if __name__ == "__main__":
   print(F'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')