



from __future__ import annotations

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	NDArray[floataa]			,	lowerCAmelCase__	:	NDArray[floataa]			,	lowerCAmelCase__	:	list[int]			,	lowerCAmelCase__	:	int			,	)       ->							list[float]:

							'''simple docstring'''






							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(lowerCAmelCase__				)

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

							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(lowerCAmelCase__				)

							if len(lowerCAmelCase__				) != rowsa:
														A      						=    (
														    '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'				)

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

							A						,   A      						=    table.shape

							strictly_diagonally_dominant(lowerCAmelCase__				)

							# Iterates the whole matrix for given number of times
							for _ in range(lowerCAmelCase__				):
														A      						=    []
														for row in range(lowerCAmelCase__				):
																					A      						=    0
																					for col in range(lowerCAmelCase__				):
																												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(lowerCAmelCase__				)
														A      						=    new_val

							return [float(lowerCAmelCase__				) for i in new_val]






def  lowerCamelCase_	(      lowerCAmelCase__	:	NDArray[floataa]				)       ->							bool:

							'''simple docstring'''






							A						,   A      						=    table.shape

							A      						=    True

							for i in range(0			,	lowerCAmelCase__				):
														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()







from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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


