
'''simple docstring'''


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import torch

class 	__lowercase				(  TensorFormatter[Mapping, "torch.Tensor", Mapping]    ):

			def __init__(self			,   A=None			,   **A      ):
								super().__init__(features=A      )
								lowerCamelCase_					:      Optional[int]     =		torch_tensor_kwargs
								import torch  # noqa import torch at initialization

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

								if isinstance(A			,   A      ) and column:
													if all(
													    isinstance(A			,   torch.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype
													    for x in column      ):
																		return torch.stack(A      )
								return column

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

								if isinstance(A			,   (str, bytes, type(A      ))      ):
													return value
								elif isinstance(A			,   (np.character, np.ndarray)      ) and np.issubdtype(value.dtype			,   np.character      ):
													return value.tolist()

								lowerCamelCase_					:      Optional[int]     =		{}

								if isinstance(A			,   (np.number, np.ndarray)      ) and np.issubdtype(value.dtype			,   np.integer      ):
													lowerCamelCase_					:      Union[str, Any]     =		{'''dtype''': torch.intaa}
								elif isinstance(A			,   (np.number, np.ndarray)      ) and np.issubdtype(value.dtype			,   np.floating      ):
													lowerCamelCase_					:      str     =		{'''dtype''': torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
													import PIL.Image

													if isinstance(A			,   PIL.Image.Image      ):
																		lowerCamelCase_					:      Optional[int]     =		np.asarray(A      )
								return torch.tensor(A			,   **{**default_dtype, **self.torch_tensor_kwargs}      )

			def        UpperCAmelCase__ (self			,   A      ):
								import torch

								# support for torch, tf, jax etc.
								if hasattr(A			,   '''__array__'''      ) and not isinstance(A			,   torch.Tensor      ):
													lowerCamelCase_					:      str     =		data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(A			,   np.ndarray      ):
													if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																		return self._consolidate([self.recursive_tensorize(A      ) for substruct in data_struct]      )
								elif isinstance(A			,   (list, tuple)      ):
													return self._consolidate([self.recursive_tensorize(A      ) for substruct in data_struct]      )
								return self._tensorize(A      )

			def        UpperCAmelCase__ (self			,   A      ):
								return map_nested(self._recursive_tensorize			,   A			,   map_list=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[Any]     =		self.numpy_arrow_extractor().extract_row(A      )
								lowerCamelCase_					:      List[Any]     =		self.python_features_decoder.decode_row(A      )
								return self.recursive_tensorize(A      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[Any]     =		self.numpy_arrow_extractor().extract_column(A      )
								lowerCamelCase_					:      List[Any]     =		self.python_features_decoder.decode_column(A			,   pa_table.column_names[0]      )
								lowerCamelCase_					:      Union[str, Any]     =		self.recursive_tensorize(A      )
								lowerCamelCase_					:      int     =		self._consolidate(A      )
								return column






			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[Any]     =		self.numpy_arrow_extractor().extract_batch(A      )
								lowerCamelCase_					:      Optional[int]     =		self.python_features_decoder.decode_batch(A      )
								lowerCamelCase_					:      str     =		self.recursive_tensorize(A      )
								for column_name in batch:
													lowerCamelCase_					:      Union[str, Any]     =		self._consolidate(batch[column_name]      )
								return batch




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		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
										lowerCamelCase_					:      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
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-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
										lowerCamelCase_					:      Tuple     =		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
										lowerCamelCase_					:      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				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

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

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      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.'''				)


