
from __future__ import annotations

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



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			):
					'''simple docstring'''
					lowercase	,						lowercase							=						coefficient_matrix.shape
					lowercase	,						lowercase							=						constant_matrix.shape

					if rowsa != colsa:
										lowercase							=						f'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
										raise ValueError(lowerCAmelCase__		)

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

					if rowsa != rowsa:
										lowercase							=						(
										    '''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:
										lowercase							=						(
										    '''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'''		)

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

					lowercase	,						lowercase							=						table.shape

					strictly_diagonally_dominant(lowerCAmelCase__		)

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

					return [float(lowerCAmelCase__		) for i in new_val]



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase	,						lowercase							=						table.shape

					lowercase							=						True

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


_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__)






