





'''simple docstring'''

def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(__SCREAMING_SNAKE_CASE       )       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Dict       ):

    # Base Case
    if index == len(__SCREAMING_SNAKE_CASE       ):
        return True

    # Recursive Step
    for i in range(__SCREAMING_SNAKE_CASE       ):
        if valid_coloring(graph[index]						,       __SCREAMING_SNAKE_CASE						,       __SCREAMING_SNAKE_CASE       ):
            # Color current vertex
            A__				=			i
            # Validate coloring
            if util_color(__SCREAMING_SNAKE_CASE						,       __SCREAMING_SNAKE_CASE						,       __SCREAMING_SNAKE_CASE						,       index + 1       ):
                return True
            # Backtrack
            A__				=			-1
    return False





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Tuple       ):
    A__				=			[-1] * len(__SCREAMING_SNAKE_CASE       )

    if util_color(__SCREAMING_SNAKE_CASE						,       __SCREAMING_SNAKE_CASE						,       __SCREAMING_SNAKE_CASE						,       0       ):
        return colored_vertices

    return []





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

