



def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      if not len(__UpperCamelCase ) == len(__UpperCamelCase ) == 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
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		equationa
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		equationa

      # Calculate the determinants of the matrices
      lowerCamelCase								=		aa * ba - aa * ba
      lowerCamelCase								=		ca * ba - ca * ba
      lowerCamelCase								=		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:
                  lowerCamelCase								=		determinant_x / determinant
                  lowerCamelCase								=		determinant_y / determinant
                  # Non-Trivial Solution (Consistent system)
                  return (x, y)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Any	     =		[
         'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CLIPSegModel',
         'CLIPSegPreTrainedModel',
         'CLIPSegTextModel',
         'CLIPSegVisionModel',
         'CLIPSegForImageSegmentation',
     ]

if TYPE_CHECKING:
     from .configuration_clipseg import (
         CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPSegConfig,
         CLIPSegTextConfig,
         CLIPSegVisionConfig,
     )
     from .processing_clipseg import CLIPSegProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clipseg import (
              CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPSegForImageSegmentation,
              CLIPSegModel,
              CLIPSegPreTrainedModel,
              CLIPSegTextModel,
              CLIPSegVisionModel,
          )

else:
     import sys

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)