





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""MobileBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MobileBertForMaskedLM""",
          """MobileBertForMultipleChoice""",
          """MobileBertForNextSentencePrediction""",
          """MobileBertForPreTraining""",
          """MobileBertForQuestionAnswering""",
          """MobileBertForSequenceClassification""",
          """MobileBertForTokenClassification""",
          """MobileBertLayer""",
          """MobileBertModel""",
          """MobileBertPreTrainedModel""",
          """load_tf_weights_in_mobilebert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFMobileBertForMaskedLM""",
          """TFMobileBertForMultipleChoice""",
          """TFMobileBertForNextSentencePrediction""",
          """TFMobileBertForPreTraining""",
          """TFMobileBertForQuestionAnswering""",
          """TFMobileBertForSequenceClassification""",
          """TFMobileBertForTokenClassification""",
          """TFMobileBertMainLayer""",
          """TFMobileBertModel""",
          """TFMobileBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mobilebert import (
          MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileBertConfig,
          MobileBertOnnxConfig,
      )
      from .tokenization_mobilebert import MobileBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mobilebert_fast import MobileBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilebert import (
                MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileBertForMaskedLM,
                MobileBertForMultipleChoice,
                MobileBertForNextSentencePrediction,
                MobileBertForPreTraining,
                MobileBertForQuestionAnswering,
                MobileBertForSequenceClassification,
                MobileBertForTokenClassification,
                MobileBertLayer,
                MobileBertModel,
                MobileBertPreTrainedModel,
                load_tf_weights_in_mobilebert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilebert import (
                TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileBertForMaskedLM,
                TFMobileBertForMultipleChoice,
                TFMobileBertForNextSentencePrediction,
                TFMobileBertForPreTraining,
                TFMobileBertForQuestionAnswering,
                TFMobileBertForSequenceClassification,
                TFMobileBertForTokenClassification,
                TFMobileBertMainLayer,
                TFMobileBertModel,
                TFMobileBertPreTrainedModel,
            )

else:
      import sys

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













from __future__ import annotations

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	coefficient_matrix.shape
   A_      ,				A_     :				Tuple			      =	constant_matrix.shape

   if rowsa != colsa:
      A_     :				int			      =	f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if colsa != 1:
      A_     :				List[Any]			      =	f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if rowsa != rowsa:
      A_     :				str			      =	(
          """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_     :				Any			      =	(
          """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_     :				NDArray[floataa]			      =	np.concatenate(
       (coefficient_matrix, constant_matrix)  ,axis=1     )

   A_      ,				A_     :				str			      =	table.shape

   strictly_diagonally_dominant(_lowerCAmelCase     )

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

   return [float(_lowerCAmelCase     ) for i in new_val]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				str			      =	table.shape

   A_     :				Any			      =	True

   for i in range(0  ,_lowerCAmelCase     ):
      A_     :				Optional[Any]			      =	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()






