



"""simple docstring"""


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

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


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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:List[Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:str=7_68 )    ->      Dict:
												super().__init__(lowercase_ )
												UpperCAmelCase					=			proj_size

												UpperCAmelCase					=			CLIPVisionModel(lowercase_ )
												UpperCAmelCase					=			PaintByExampleMapper(lowercase_ )
												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      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Optional[Any]       ,  lowercase_			:str=False )    ->      Tuple:
												UpperCAmelCase					=			self.model(pixel_values=lowercase_ )
												UpperCAmelCase					=			clip_output.pooler_output
												UpperCAmelCase					=			self.mapper(latent_states[:, None] )
												UpperCAmelCase					=			self.final_layer_norm(lowercase_ )
												UpperCAmelCase					=			self.proj_out(lowercase_ )
												if return_uncond_vector:
																		return latent_states, self.uncond_vector

												return latent_states







class 				A_      (					nn.Module    ):



						"""simple docstring"""






						def __init__( self			:List[str]       ,  lowercase_			:Any )    ->      Tuple:
												super().__init__()
												UpperCAmelCase					=			(config.num_hidden_layers + 1) // 5
												UpperCAmelCase					=			config.hidden_size
												UpperCAmelCase					=			1
												UpperCAmelCase					=			nn.ModuleList(
												    [
												        BasicTransformerBlock(lowercase_       ,  lowercase_       ,  lowercase_       ,  activation_fn='gelu'       ,  attention_bias=lowercase_ )
												        for _ in range(lowercase_ )
												    ] )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Union[str, Any] )    ->      Union[str, Any]:
												for block in self.blocks:
																		UpperCAmelCase					=			block(lowercase_ )

												return hidden_states










"""simple docstring"""


def 			_lowerCAmelCase				(       ):
						for n in range(1		,     1000000						):
												yield n * (n + 1) // 2

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			1
						UpperCAmelCase					=			2
						while i * i <= n:
												UpperCAmelCase					=			0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count

def 			_lowerCAmelCase				(       ):
						return next(i for i in triangle_number_generator() if count_divisors(lowercase_						) > 500						)


if __name__ == "__main__":
				print(solution())





