


# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCAmelCase_   :				str						      = logging.get_logger(__name__)

UpperCAmelCase_   :				Dict[Optional[str], Type[Formatter]]						      = {}
UpperCAmelCase_   :				Dict[Optional[str], str]						      = {}
UpperCAmelCase_   :				Dict[Optional[str], Exception]						      = {}







def  SCREAMING_SNAKE_CASE_		(    __A				:    type						,					__A				:    Optional[str]						,					__A				:    Optional[List[str]] = None						,					)			->	int:

       """simple docstring"""

       a_      :	str							=						aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""       )
       a_      :	Tuple							=						formatter_cls
       for alias in set(aliases + [format_type]       ):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""       )
              a_      :	int							=						format_type







def  SCREAMING_SNAKE_CASE_		(    __A				:    Exception						,					__A				:    Optional[str]						,					__A				:    Optional[List[str]] = None       )			->	Tuple:

       """simple docstring"""

       a_      :	List[str]							=						aliases if aliases is not None else []
       for alias in set(aliases + [format_type]       ):
              a_      :	Union[str, Any]							=						unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
 from .torch_formatter import TorchFormatter

 _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
 UpperCAmelCase_   :				Optional[Any]						      = ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
 _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
 from .tf_formatter import TFFormatter

 _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
 UpperCAmelCase_   :				List[str]						      = ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
 _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
 from .jax_formatter import JaxFormatter

 _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
 UpperCAmelCase_   :				Tuple						      = ValueError('JAX needs to be installed to be able to return JAX arrays.')
 _register_unavailable_formatter(_jax_error, 'jax', aliases=[])







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[str]       )			->	Optional[str]:

       """simple docstring"""

       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[str]						,					**__A				:    Tuple       )			->	Formatter:

       """simple docstring"""

       a_      :	str							=						get_format_type_from_alias(__A       )
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**__A       )
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None       )}, but got '{format_type}'"""       )






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


