






'''simple docstring'''

from __future__ import annotations

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



def 							snake_case					(     a_							: NDArray[floataa]    ,   a_							: NDArray[floataa]    ,   a_							: list[int]    ,   a_							: int    ,   )       ->				list[float]:





						"""simple docstring"""

						UpperCamelCase_				:   Optional[int]					    =							coefficient_matrix.shape
						UpperCamelCase_				:   Optional[Any]					    =							constant_matrix.shape

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

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

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

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

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

						UpperCamelCase_				:   NDArray[floataa]					    =							np.concatenate(
						    (coefficient_matrix, constant_matrix)    ,   axis=1	)

						UpperCamelCase_				:   Any					    =							table.shape

						strictly_diagonally_dominant(a_	)

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

						return [float(a_	) for i in new_val]



def 							snake_case					(     a_							: NDArray[floataa]	)       ->				bool:





						"""simple docstring"""

						UpperCamelCase_				:   List[Any]					    =							table.shape

						UpperCamelCase_				:   Dict					    =							True

						for i in range(0    ,   a_	):
												UpperCamelCase_				:   str					    =							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_tf_available, is_torch_available


UpperCamelCase							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "TFData2VecVisionForImageClassification",
          "TFData2VecVisionForSemanticSegmentation",
          "TFData2VecVisionModel",
          "TFData2VecVisionPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

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






