


'''simple docstring'''






import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def A_					(				__SCREAMING_SNAKE_CASE :					NDArray[floataa]	,   __SCREAMING_SNAKE_CASE :					NDArray[floataa]       )     ->  NDArray[floataa]:
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Optional[int]			   = np.shape(__SCREAMING_SNAKE_CASE       )

					__SCREAMING_SNAKE_CASE					:							NDArray[floataa]			   = np.zeros((rows, 1)	,   dtype=__SCREAMING_SNAKE_CASE       )
					for row in reversed(range(__SCREAMING_SNAKE_CASE       )       ):
										__SCREAMING_SNAKE_CASE					:							Tuple			   = 0
										for col in range(row + 1	,   __SCREAMING_SNAKE_CASE       ):
															total += coefficients[row, col] * x[col]

										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = (vector[row] - total) / coefficients[row, row]

					return x


def A_					(				__SCREAMING_SNAKE_CASE :					NDArray[floataa]	,   __SCREAMING_SNAKE_CASE :					NDArray[floataa]       )     ->  NDArray[floataa]:
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = np.shape(__SCREAMING_SNAKE_CASE       )
					if rows != columns:
										return np.array(()	,   dtype=__SCREAMING_SNAKE_CASE       )

					# augmented matrix
					__SCREAMING_SNAKE_CASE					:							NDArray[floataa]			   = np.concatenate((coefficients, vector)	,   axis=1       )
					__SCREAMING_SNAKE_CASE					:							Tuple			   = augmented_mat.astype('''float64'''       )

					# scale the matrix leaving it triangular
					for row in range(rows - 1       ):
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = augmented_mat[row, row]
										for col in range(row + 1	,   __SCREAMING_SNAKE_CASE       ):
															__SCREAMING_SNAKE_CASE					:							Tuple			   = augmented_mat[col, row] / pivot
															augmented_mat[col, :] -= factor * augmented_mat[row, :]

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = retroactive_resolution(
					    augmented_mat[:, 0:columns]	,   augmented_mat[:, columns : columns + 1]       )

					return x


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     [
						    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DecisionTransformerGPT2Model""",
						    """DecisionTransformerGPT2PreTrainedModel""",
						    """DecisionTransformerModel""",
						    """DecisionTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DecisionTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_decision_transformer import (
												    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DecisionTransformerGPTaModel,
												    DecisionTransformerGPTaPreTrainedModel,
												    DecisionTransformerModel,
												    DecisionTransformerPreTrainedModel,
												)


else:
						import sys

						_A      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


