import math
from collections.abc import Callable







def      _a					(   lowercase__	:      Callable[[float], float]  ,	lowercase__	:      float  ,	lowercase__	:      float			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					float      =		xa
     SCREAMING_SNAKE_CASE__					:					float      =		xa
     while True:
          if x_n == x_na or function(lowercase__			) == function(lowercase__			):
               raise ZeroDivisionError('float division by zero, could not find root'			)
          SCREAMING_SNAKE_CASE__					:					float      =		x_na - (
              function(lowercase__			) / ((function(lowercase__			) - function(lowercase__			)) / (x_na - x_n))
          )
          if abs(x_na - x_na			) < 10**-5:
               return x_na
          SCREAMING_SNAKE_CASE__					:					Dict      =		x_na
          SCREAMING_SNAKE_CASE__					:					List[str]      =		x_na







def      _a					(   lowercase__	:      float			):







     '''simple docstring'''


     return math.pow(lowercase__  ,	3			) - (2 * x) - 5


if __name__ == "__main__":
      print(intersection(f, 3, 3.5))









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

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


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

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



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

