



"""simple docstring"""
def       _snake_case						(						UpperCamelCase						:		dict						):
 UpperCAmelCase	:					set[int]	=	set()
 # To detect a back edge, keep track of vertices currently in the recursion stack
 UpperCAmelCase	:					set[int]	=	set()
 return any(
     node not in visited and depth_first_search(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
     for node in graph						)

def       _snake_case						(						UpperCamelCase						:		dict			,		UpperCamelCase						:		int			,		UpperCamelCase						:		set			,		UpperCamelCase						:		set						):
 visited.add(UpperCamelCase						)
 rec_stk.add(UpperCamelCase						)

 for node in graph[vertex]:
  if node not in visited:
   if depth_first_search(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						):
    return True
  elif node in rec_stk:
   return True

    # The node needs to be removed from recursion stack before function ends
 rec_stk.remove(UpperCamelCase						)
 return False


if __name__ == "__main__":
      from doctest import testmod

      testmod()


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

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


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

