





def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			list[int]	):

   # Check if the input is valid
   if not len(lowerCAmelCase_	) == len(lowerCAmelCase_	) == 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_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

