





'''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()









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


