from __future__ import annotations







def  __UpperCamelCase    (						A							):
     if len(A							) < 2:
          raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''							)
     if any(i <= 0 for i in nums							):
          raise ValueError('''All values must be greater than 0'''							)
     UpperCamelCase__										=      nums.copy()
     copy_nums.sort()
     return copy_nums[-1] < sum(copy_nums[:-1]							)


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



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

       UpperCamelCase__										=      CLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      PaintByExampleMapper(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.LayerNorm(config.hidden_size	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				self.proj_size	)

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      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 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



