






from __future__ import annotations

from decimal import Decimal

from numpy import array


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
    # Calculate the determinant of the matrix
    SCREAMING_SNAKE_CASE       :      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
    SCREAMING_SNAKE_CASE       :      Any							=    [[0.0, 0.0], [0.0, 0.0]]
    SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    matrix[1][1], matrix[0][0]
    SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    -matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(lowercase				) == 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
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    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
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [
        [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				)],
    ]
    SCREAMING_SNAKE_CASE       :      str							=    (d(matrix[1][1]				) * d(matrix[2][2]				)) - (
        d(matrix[1][2]				) * d(matrix[2][1]				)
    )
    SCREAMING_SNAKE_CASE       :      List[Any]							=    -(
        (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
    )
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    (d(matrix[1][0]				) * d(matrix[2][1]				)) - (
        d(matrix[1][1]				) * d(matrix[2][0]				)
    )
    SCREAMING_SNAKE_CASE       :      Any							=    -(
        (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
    )
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    (d(matrix[0][0]				) * d(matrix[2][2]				)) - (
        d(matrix[0][2]				) * d(matrix[2][0]				)
    )
    SCREAMING_SNAKE_CASE       :      str							=    -(
        (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
    )
    SCREAMING_SNAKE_CASE       :      Dict							=    (d(matrix[0][1]				) * d(matrix[1][2]				)) - (
        d(matrix[0][2]				) * d(matrix[1][1]				)
    )
    SCREAMING_SNAKE_CASE       :      List[Any]							=    -(
        (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
    )
    SCREAMING_SNAKE_CASE       :      int							=    (d(matrix[0][0]				) * d(matrix[1][1]				)) - (
        d(matrix[0][1]				) * d(matrix[1][0]				)
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    SCREAMING_SNAKE_CASE       :      int							=    array(lowercase				)
    for i in range(3				):
      for j in range(3				):
        SCREAMING_SNAKE_CASE       :      List[str]							=    cofactor_matrix[j][i]

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

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














from typing import TYPE_CHECKING

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


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

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






