


def 			_a  (				SCREAMING_SNAKE_CASE	:				list[int]    , SCREAMING_SNAKE_CASE	:				list[int]     ):







	"""simple docstring"""



	if not len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     ) == 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__					:    Optional[int]          =   equationa
	UpperCamelCase__					:    Tuple          =   equationa

	# Calculate the determinants of the matrices
	UpperCamelCase__					:    Dict          =   aa * ba - aa * ba
	UpperCamelCase__					:    str          =   ca * ba - ca * ba
	UpperCamelCase__					:    Union[str, Any]          =   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__					:    List[str]          =   determinant_x / determinant
			UpperCamelCase__					:    List[Any]          =   determinant_y / determinant
			# Non-Trivial Solution (Consistent system)
			return (x, y)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

       __UpperCamelCase       :       List[Any]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


