

'''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_torch_available,
)


UpperCAmelCase				 =   {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  UpperCAmelCase				 =   [
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

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