





'''simple docstring'''







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

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


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


class 					__a      (lowerCamelCase     ):

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     List[str]=7_68   )					->       Tuple:







													"""simple docstring"""
													super().__init__(__magic_name__   )
													UpperCAmelCase_			:      str     						=  proj_size

													UpperCAmelCase_			:      Dict     						=  CLIPVisionModel(__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  PaintByExampleMapper(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  nn.LayerNorm(config.hidden_size   )
													UpperCAmelCase_			:      Dict     						=  nn.Linear(config.hidden_size       ,     self.proj_size   )

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



							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Tuple=False   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.model(pixel_values=__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  clip_output.pooler_output
													UpperCAmelCase_			:      Any     						=  self.mapper(latent_states[:, None]   )
													UpperCAmelCase_			:      Dict     						=  self.final_layer_norm(__magic_name__   )
													UpperCAmelCase_			:      str     						=  self.proj_out(__magic_name__   )
													if return_uncond_vector:
																			return latent_states, self.uncond_vector

													return latent_states





class 					__a      (nn.Module     ):

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													super().__init__()
													UpperCAmelCase_			:      int     						=  (config.num_hidden_layers + 1) // 5
													UpperCAmelCase_			:      Dict     						=  config.hidden_size
													UpperCAmelCase_			:      Optional[int]     						=  1
													UpperCAmelCase_			:      Union[str, Any]     						=  nn.ModuleList(
													    [
													        BasicTransformerBlock(__magic_name__       ,     __magic_name__       ,     __magic_name__       ,     activation_fn='''gelu'''       ,     attention_bias=__magic_name__   )
													        for _ in range(__magic_name__   )
													    ]   )



							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													for block in self.blocks:
																			UpperCAmelCase_			:      Any     						=  block(__magic_name__   )

													return hidden_states












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

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





