


'''simple docstring'''
from __future__ import annotations

from decimal import Decimal

from numpy import array





def 					a__       (					lowerCAmelCase__						)    ->	list[list[float]]:
    UpperCAmelCase__			:		Optional[int]													=				Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(lowerCAmelCase__						) == 2 and len(matrix[0]						) == 2 and len(matrix[1]						) == 2:
        # Calculate the determinant of the matrix
        UpperCAmelCase__			:		Union[str, Any]													=				float(
            d(matrix[0][0]						) * d(matrix[1][1]						) - d(matrix[1][0]						) * d(matrix[0][1]						)						)
        if determinant == 0:
            raise ValueError('''This matrix has no inverse.'''						)

        # Creates a copy of the matrix with swapped positions of the elements
        UpperCAmelCase__			:		Optional[int]													=				[[0.0, 0.0], [0.0, 0.0]]
        UpperCAmelCase__						,						UpperCAmelCase__			:		List[str]													=				matrix[1][1], matrix[0][0]
        UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				-matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(lowerCAmelCase__						)						) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(lowerCAmelCase__						) == 3
        and len(matrix[0]						) == 3
        and len(matrix[1]						) == 3
        and len(matrix[2]						) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        UpperCAmelCase__			:		Any													=				float(
            (
                (d(matrix[0][0]						) * d(matrix[1][1]						) * d(matrix[2][2]						))
                + (d(matrix[0][1]						) * d(matrix[1][2]						) * d(matrix[2][0]						))
                + (d(matrix[0][2]						) * d(matrix[1][0]						) * d(matrix[2][1]						))
            )
            - (
                (d(matrix[0][2]						) * d(matrix[1][1]						) * d(matrix[2][0]						))
                + (d(matrix[0][1]						) * d(matrix[1][0]						) * d(matrix[2][2]						))
                + (d(matrix[0][0]						) * d(matrix[1][2]						) * d(matrix[2][1]						))
            )						)
        if determinant == 0:
            raise ValueError('''This matrix has no inverse.'''						)

        # Creating cofactor matrix
        UpperCAmelCase__			:		Dict													=				[
            [d(0.0						), d(0.0						), d(0.0						)],
            [d(0.0						), d(0.0						), d(0.0						)],
            [d(0.0						), d(0.0						), d(0.0						)],
        ]
        UpperCAmelCase__			:		Any													=				(d(matrix[1][1]						) * d(matrix[2][2]						)) - (
            d(matrix[1][2]						) * d(matrix[2][1]						)
        )
        UpperCAmelCase__			:		List[str]													=				-(
            (d(matrix[1][0]						) * d(matrix[2][2]						)) - (d(matrix[1][2]						) * d(matrix[2][0]						))
        )
        UpperCAmelCase__			:		Dict													=				(d(matrix[1][0]						) * d(matrix[2][1]						)) - (
            d(matrix[1][1]						) * d(matrix[2][0]						)
        )
        UpperCAmelCase__			:		str													=				-(
            (d(matrix[0][1]						) * d(matrix[2][2]						)) - (d(matrix[0][2]						) * d(matrix[2][1]						))
        )
        UpperCAmelCase__			:		List[Any]													=				(d(matrix[0][0]						) * d(matrix[2][2]						)) - (
            d(matrix[0][2]						) * d(matrix[2][0]						)
        )
        UpperCAmelCase__			:		List[Any]													=				-(
            (d(matrix[0][0]						) * d(matrix[2][1]						)) - (d(matrix[0][1]						) * d(matrix[2][0]						))
        )
        UpperCAmelCase__			:		Any													=				(d(matrix[0][1]						) * d(matrix[1][2]						)) - (
            d(matrix[0][2]						) * d(matrix[1][1]						)
        )
        UpperCAmelCase__			:		int													=				-(
            (d(matrix[0][0]						) * d(matrix[1][2]						)) - (d(matrix[0][2]						) * d(matrix[1][0]						))
        )
        UpperCAmelCase__			:		Optional[int]													=				(d(matrix[0][0]						) * d(matrix[1][1]						)) - (
            d(matrix[0][1]						) * d(matrix[1][0]						)
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        UpperCAmelCase__			:		Optional[Any]													=				array(lowerCAmelCase__						)
        for i in range(3						):
            for j in range(3						):
                UpperCAmelCase__			:		Optional[Any]													=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        UpperCAmelCase__			:		Optional[int]													=				array(lowerCAmelCase__						)
        for i in range(3						):
            for j in range(3						):
                inverse_matrix[i][j] /= d(lowerCAmelCase__						)

        # Calculate the inverse of the matrix
        return [[float(d(lowerCAmelCase__						)						) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''						)




'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__    					=      {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''DistilBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DistilBertForMaskedLM''',
      '''DistilBertForMultipleChoice''',
      '''DistilBertForQuestionAnswering''',
      '''DistilBertForSequenceClassification''',
      '''DistilBertForTokenClassification''',
      '''DistilBertModel''',
      '''DistilBertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFDistilBertForMaskedLM''',
      '''TFDistilBertForMultipleChoice''',
      '''TFDistilBertForQuestionAnswering''',
      '''TFDistilBertForSequenceClassification''',
      '''TFDistilBertForTokenClassification''',
      '''TFDistilBertMainLayer''',
      '''TFDistilBertModel''',
      '''TFDistilBertPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''FlaxDistilBertForMaskedLM''',
      '''FlaxDistilBertForMultipleChoice''',
      '''FlaxDistilBertForQuestionAnswering''',
      '''FlaxDistilBertForSequenceClassification''',
      '''FlaxDistilBertForTokenClassification''',
      '''FlaxDistilBertModel''',
      '''FlaxDistilBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
