






'''simple docstring'''
from __future__ import annotations

import numpy as np




def 		__SCREAMING_SNAKE_CASE					(  lowerCamelCase							):


   """simple docstring"""




   lowercase_	:    Tuple           =     np.shape(_UpperCamelCase							)
   if rows != columns:
      lowercase_	:    List[str]           =     (
          "'table' has to be of square shaped array but got a "
          F"""{rows}x{columns} array:\n{table}"""
      )
      raise ValueError(_UpperCamelCase							)

   lowercase_	:    Dict           =     np.zeros((rows, columns)							)
   lowercase_	:    Any           =     np.zeros((rows, columns)							)
   for i in range(_UpperCamelCase							):
      for j in range(_UpperCamelCase							):
         lowercase_	:    List[str]           =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase							)							)
         if upper[j][j] == 0:
            raise ArithmeticError("No LU decomposition exists"							)
         lowercase_	:    str           =     (table[i][j] - total) / upper[j][j]
      lowercase_	:    Tuple           =     1
      for j in range(_UpperCamelCase						,		_UpperCamelCase							):
         lowercase_	:    int           =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase							)							)
         lowercase_	:    Optional[int]           =     table[i][j] - total
   return lower, upper


if __name__ == "__main__":
 import doctest

 doctest.testmod()












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




