




def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      []
  if len(__lowerCAmelCase				) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase				)				):
    lowerCAmelCase__										=      nums.pop(0				)
    lowerCAmelCase__										=      permute(__lowerCAmelCase				)
    for perm in permutations:
      perm.append(__lowerCAmelCase				)
    result.extend(__lowerCAmelCase				)
    nums.append(__lowerCAmelCase				)
  return result
def 		a_							( __lowerCAmelCase				):

  def backtrack(__lowerCAmelCase				):
    if start == len(__lowerCAmelCase				) - 1:
      output.append(nums[:]				)
    else:
      for i in range(__lowerCAmelCase   , len(__lowerCAmelCase				)				):
        lowerCAmelCase__				,      lowerCAmelCase__										=      nums[i], nums[start]
        backtrack(start + 1				)
        lowerCAmelCase__				,      lowerCAmelCase__										=      nums[i], nums[start]  # backtrack

  lowerCAmelCase__										=      []
  backtrack(0				)
  return output


if __name__ == "__main__":
      import doctest

      # use res to print the data in permute2 function
      __magic_name__  :  Optional[Any]					=       permutea([1, 2, 3])
      print(res)
      doctest.testmod()







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

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


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





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

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



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

