




# 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        __snake_case  (TensorFormatter[Mapping, "torch.Tensor", Mapping]		):



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		Union[str, Any]=None		,	**_UpperCAmelCase      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								super().__init__(features=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      torch_tensor_kwargs
								import torch  # noqa import torch at initialization



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		int      )  ->    Optional[Any]:





								'''simple docstring'''




								import torch

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								import torch

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

								_lowerCAmelCase    :    Dict										=      {}

								if isinstance(_UpperCAmelCase		,	(np.number, np.ndarray)      ) and np.issubdtype(value.dtype		,	np.integer      ):
												_lowerCAmelCase    :    Optional[int]										=      {"""dtype""": torch.intaa}
								elif isinstance(_UpperCAmelCase		,	(np.number, np.ndarray)      ) and np.issubdtype(value.dtype		,	np.floating      ):
												_lowerCAmelCase    :    List[Any]										=      {"""dtype""": torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_UpperCAmelCase		,	PIL.Image.Image      ):
																_lowerCAmelCase    :    Any										=      np.asarray(_UpperCAmelCase      )
								return torch.tensor(_UpperCAmelCase		,	**{**default_dtype, **self.torch_tensor_kwargs}      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[str]      )  ->    Optional[int]:





								'''simple docstring'''




								import torch

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		dict      )  ->    str:





								'''simple docstring'''




								return map_nested(self._recursive_tensorize		,	_UpperCAmelCase		,	map_list=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		pa.Table      )  ->    Mapping:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.numpy_arrow_extractor().extract_row(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      self.python_features_decoder.decode_row(_UpperCAmelCase      )
								return self.recursive_tensorize(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		pa.Table      )  ->    "torch.Tensor":





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      self.numpy_arrow_extractor().extract_column(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      self.python_features_decoder.decode_column(_UpperCAmelCase		,	pa_table.column_names[0]      )
								_lowerCAmelCase    :    Tuple										=      self.recursive_tensorize(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self._consolidate(_UpperCAmelCase      )
								return column



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		pa.Table      )  ->    Mapping:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      self.python_features_decoder.decode_batch(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      self.recursive_tensorize(_UpperCAmelCase      )
								for column_name in batch:
												_lowerCAmelCase    :    int										=      self._consolidate(batch[column_name]      )
								return batch







from __future__ import annotations

from decimal import Decimal

from numpy import array




def 							_UpperCAmelCase  (UpperCamelCase_ : list[list[float]]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      Decimal

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

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

								# Transpose the cofactor matrix (Adjoint matrix)
								_lowerCAmelCase    :    List[str]										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																_lowerCAmelCase    :    List[Any]										=      cofactor_matrix[j][i]

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

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

