"""simple docstring"""




from __future__ import annotations

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: NDArray[floataa]    ,_lowerCamelCase		: NDArray[floataa]    ,_lowerCamelCase		: list[int]    ,_lowerCamelCase		: int    ,) ->   list[float]:
  _lowerCAmelCase		: Union[str, Any]						    =  coefficient_matrix.shape
  _lowerCAmelCase		: Optional[int]						    =  constant_matrix.shape

  if rowsa != colsa:
    _lowerCAmelCase		: int						    =  f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
    raise ValueError(_lowerCamelCase			)

  if colsa != 1:
    _lowerCAmelCase		: str						    =  f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
    raise ValueError(_lowerCamelCase			)

  if rowsa != rowsa:
    _lowerCAmelCase		: Any						    =  (
        """Coefficient and constant matrices dimensions must be nxn and nx1 but """
        f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
    )
    raise ValueError(_lowerCamelCase			)

  if len(_lowerCamelCase			) != rowsa:
    _lowerCAmelCase		: Dict						    =  (
        """Number of initial values must be equal to number of rows in coefficient """
        f"matrix but received {len(_lowerCamelCase			)} and {rowsa}"
    )
    raise ValueError(_lowerCamelCase			)

  if iterations <= 0:
    raise ValueError("""Iterations must be at least 1"""			)

  _lowerCAmelCase		: NDArray[floataa]						    =  np.concatenate(
      (coefficient_matrix, constant_matrix)    ,axis=1			)

  _lowerCAmelCase		: Optional[int]						    =  table.shape

  strictly_diagonally_dominant(_lowerCamelCase			)

  # Iterates the whole matrix for given number of times
  for _ in range(_lowerCamelCase			):
    _lowerCAmelCase		: int						    =  []
    for row in range(_lowerCamelCase			):
      _lowerCAmelCase		: str						    =  0
      for col in range(_lowerCamelCase			):
        if col == row:
          _lowerCAmelCase		: int						    =  table[row][col]
        elif col == cols - 1:
          _lowerCAmelCase		: Tuple						    =  table[row][col]
        else:
          temp += (-1) * table[row][col] * init_val[col]
      _lowerCAmelCase		: List[str]						    =  (temp + val) / denom
      new_val.append(_lowerCamelCase			)
    _lowerCAmelCase		: Optional[int]						    =  new_val

  return [float(_lowerCamelCase			) for i in new_val]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: NDArray[floataa]			) ->   bool:
  _lowerCAmelCase		: Any						    =  table.shape

  _lowerCAmelCase		: Union[str, Any]						    =  True

  for i in range(0    ,_lowerCamelCase			):
    _lowerCAmelCase		: Union[str, Any]						    =  0
    for j in range(0    ,cols - 1			):
      if i == j:
        continue
      else:
        total += table[i][j]

    if table[i][i] <= total:
      raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""			)

  return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
     import doctest

     doctest.testmod()





"""simple docstring"""




from typing import TYPE_CHECKING

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


_a   :	int 					=     {
    'configuration_altclip': [
        'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AltCLIPConfig',
        'AltCLIPTextConfig',
        'AltCLIPVisionConfig',
    ],
    'processing_altclip': ['AltCLIPProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Dict 					=     [
         'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AltCLIPPreTrainedModel',
         'AltCLIPModel',
         'AltCLIPTextModel',
         'AltCLIPVisionModel',
     ]


if TYPE_CHECKING:
     from .configuration_altclip import (
         ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AltCLIPConfig,
         AltCLIPTextConfig,
         AltCLIPVisionConfig,
     )
     from .processing_altclip import AltCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_altclip import (
              ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              AltCLIPModel,
              AltCLIPPreTrainedModel,
              AltCLIPTextModel,
              AltCLIPVisionModel,
          )


else:
     import sys

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




