

'''simple docstring'''

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

def      __magic_name__							(			A       , A				)      ->		NDArray[floataa]:
	snake_case			,			snake_case							=					np.shape(A				)

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

		snake_case							=					(vector[row] - total) / coefficients[row, row]

	return x

def      __magic_name__							(			A       , A				)      ->		NDArray[floataa]:
	snake_case			,			snake_case							=					np.shape(A				)
	if rows != columns:
		return np.array(()       , dtype=A				)

	# augmented matrix
	snake_case							=					np.concatenate((coefficients, vector)       , axis=1				)
	snake_case							=					augmented_mat.astype('float64'				)

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

	snake_case							=					retroactive_resolution(
	    augmented_mat[:, 0:columns]       , augmented_mat[:, columns : columns + 1]				)

	return x


if __name__ == "__main__":
						import doctest

						doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       Tuple						=   [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


