



'''simple docstring'''





from __future__ import annotations

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Union[str, Any] ,       )->  list[float]:
    A__ ,	A__		 =					coefficient_matrix.shape
    A__ ,	A__		 =					constant_matrix.shape

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

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

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

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

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

    A__		 =					np.concatenate(
        (coefficient_matrix, constant_matrix) ,       axis=1	)

    A__ ,	A__		 =					table.shape

    strictly_diagonally_dominant(UpperCamelCase_	)

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

    return [float(UpperCamelCase_	) for i in new_val]



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  bool:
    A__ ,	A__		 =					table.shape

    A__		 =					True

    for i in range(0 ,       UpperCamelCase_	):
        A__		 =					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()





# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase						  =		{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CpmAntForCausalLM",
       "CpmAntModel",
       "CpmAntPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

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