def    lowerCAmelCase_ (     __A,  __A							)     ->				tuple[float, float]:







      '''simple docstring'''


      if not len(__A							) == len(__A							) == 3:
            raise ValueError("Please enter a valid equation."							)
      if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
            raise ValueError("Both a & b of two equations can't be zero."							)

      # Extract the coefficients
      UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__								=  equationa
      UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__								=  equationa

      # Calculate the determinants of the matrices
      UpperCAmelCase__								=  aa * ba - aa * ba
      UpperCAmelCase__								=  ca * ba - ca * ba
      UpperCAmelCase__								=  aa * ca - aa * ca

      # Check if the system of linear equations has a solution (using Cramer's rule)
      if determinant == 0:
            if determinant_x == determinant_y == 0:
                  raise ValueError("Infinite solutions. (Consistent system)"							)
            else:
                  raise ValueError("No solution. (Inconsistent system)"							)
      else:
            if determinant_x == determinant_y == 0:
                  # Trivial solution (Inconsistent system)
                  return (0.0, 0.0)
            else:
                  UpperCAmelCase__								=  determinant_x / determinant
                  UpperCAmelCase__								=  determinant_y / determinant
                  # Non-Trivial Solution (Consistent system)
                  return (x, y)





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

