




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

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


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




class 						UpperCamelCase__      (_lowerCamelCase	):

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__	,    UpperCamelCase__=768	)	-> List[Any]:
           super().__init__(lowercase_	)
           lowerCamelCase  : List[Any]     					=   proj_size

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

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







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__=False	)	-> List[Any]:
           lowerCamelCase  : List[str]     					=   self.model(pixel_values=lowercase_	)
           lowerCamelCase  : Optional[int]     					=   clip_output.pooler_output
           lowerCamelCase  : int     					=   self.mapper(latent_states[:, None]	)
           lowerCamelCase  : str     					=   self.final_layer_norm(lowercase_	)
           lowerCamelCase  : Dict     					=   self.proj_out(lowercase_	)
           if return_uncond_vector:
                  return latent_states, self.uncond_vector

           return latent_states




class 						UpperCamelCase__      (nn.Module	):

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__	)	-> Dict:
           super().__init__()
           lowerCamelCase  : Union[str, Any]     					=   (config.num_hidden_layers + 1) // 5
           lowerCamelCase  : Optional[Any]     					=   config.hidden_size
           lowerCamelCase  : Optional[int]     					=   1
           lowerCamelCase  : List[str]     					=   nn.ModuleList(
               [
                   BasicTransformerBlock(lowercase_	,    lowercase_	,    lowercase_	,    activation_fn="gelu"	,    attention_bias=lowercase_	)
                   for _ in range(lowercase_	)
               ]	)







    def        _lowercase	(       self	,    UpperCamelCase__	)	-> Optional[int]:
           for block in self.blocks:
                  lowerCamelCase  : Optional[int]     					=   block(lowercase_	)

           return hidden_states












'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

