



"""simple docstring"""

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

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


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




class        _UpperCAmelCase       (			__lowerCAmelCase):

 def __init__(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Optional[int]=768				):
     super().__init__(lowercase_				)
     snake_case_ :     Dict				=	proj_size

     snake_case_ :     List[str]				=	CLIPVisionModel(lowercase_				)
     snake_case_ :     Tuple				=	PaintByExampleMapper(lowercase_				)
     snake_case_ :     List[str]				=	nn.LayerNorm(config.hidden_size				)
     snake_case_ :     Optional[Any]				=	nn.Linear(config.hidden_size  ,     self.proj_size				)

     # uncondition for scaling
     snake_case_ :     List[Any]				=	nn.Parameter(torch.randn((1, 1, self.proj_size)				)				)






 def 						_snake_case					(     self		:       int  ,     lowercase_		:       str  ,     lowercase_		:       List[str]=False				):
     snake_case_ :     str				=	self.model(pixel_values=lowercase_				)
     snake_case_ :     Union[str, Any]				=	clip_output.pooler_output
     snake_case_ :     int				=	self.mapper(latent_states[:, None]				)
     snake_case_ :     int				=	self.final_layer_norm(lowercase_				)
     snake_case_ :     Optional[Any]				=	self.proj_out(lowercase_				)
     if return_uncond_vector:
         return latent_states, self.uncond_vector

     return latent_states





class        _UpperCAmelCase       (			nn.Module):

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






 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, Any]				):





     for block in self.blocks:
         snake_case_ :     str				=	block(lowercase_				)

     return hidden_states






"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


if __name__ == "__main__":
 print(f"{solution() = }")


