





'''simple docstring'''


from __future__ import annotations

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


def 						__a(SCREAMING_SNAKE_CASE_	:       NDArray[floataa]		,							SCREAMING_SNAKE_CASE_	:       NDArray[floataa]		,							SCREAMING_SNAKE_CASE_	:       list[int]		,							SCREAMING_SNAKE_CASE_	:       int		,							):



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase						      =   coefficient_matrix.shape
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   constant_matrix.shape

    if rowsa != colsa:
        _lowerCAmelCase						      =   F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
        raise ValueError(SCREAMING_SNAKE_CASE_     )

    if colsa != 1:
        _lowerCAmelCase						      =   F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
        raise ValueError(SCREAMING_SNAKE_CASE_     )

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

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

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

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

    _lowerCAmelCase      ,      _lowerCAmelCase						      =   table.shape

    strictly_diagonally_dominant(SCREAMING_SNAKE_CASE_     )

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

    return [float(SCREAMING_SNAKE_CASE_     ) for i in new_val]


def 						__a(SCREAMING_SNAKE_CASE_	:       NDArray[floataa]     ):



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase						      =   table.shape

    _lowerCAmelCase						      =   True

    for i in range(0		,							SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   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()






from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





