

'''simple docstring'''

import unittest

import numpy as np




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = None  ,	):
 UpperCAmelCase	:     Optional[int]						 =			np.shape(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			np.shape(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			np.shape(UpperCAmelCase_     )

 if shape_a[0] != shape_b[0]:
  UpperCAmelCase	:     Optional[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(UpperCAmelCase_     )

 if shape_b[1] != shape_c[1]:
  UpperCAmelCase	:     Tuple						 =			(
      '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(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			pseudo_inv
 if a_inv is None:
  try:
   UpperCAmelCase	:     Tuple						 =			np.linalg.inv(UpperCAmelCase_     )
  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 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Tuple	)						->     None:
      UpperCAmelCase	:     List[Any]						 =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]	)
      UpperCAmelCase	:     Optional[int]						 =			np.array([[0, 3], [3, 0], [2, 3]]	)
      UpperCAmelCase	:     Tuple						 =			np.array([[2, 1], [6, 3]]	)

      UpperCAmelCase	:     Tuple						 =			schur_complement(lowercase_					,      lowercase_					,      lowercase_	)

      UpperCAmelCase	:     List[str]						 =			np.block([[a, b], [b.T, c]]	)

      UpperCAmelCase	:     Tuple						 =			np.linalg.det(lowercase_	)
      UpperCAmelCase	:     Tuple						 =			np.linalg.det(lowercase_	)
      UpperCAmelCase	:     Tuple						 =			np.linalg.det(lowercase_	)

      self.assertAlmostEqual(lowercase_					,      det_a * det_s	)


     def        UpperCAmelCase_	(		self			:					Any	)						->     None:
      UpperCAmelCase	:     Union[str, Any]						 =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]	)
      UpperCAmelCase	:     str						 =			np.array([[0, 3], [3, 0], [2, 3]]	)
      UpperCAmelCase	:     Tuple						 =			np.array([[2, 1], [6, 3]]	)

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




     def        UpperCAmelCase_	(		self			:					Dict	)						->     None:
      UpperCAmelCase	:     Dict						 =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]	)
      UpperCAmelCase	:     int						 =			np.array([[0, 3], [3, 0], [2, 3]]	)
      UpperCAmelCase	:     Union[str, Any]						 =			np.array([[2, 1, 3], [6, 3, 5]]	)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()







'''simple docstring'''

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     Tuple						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 UpperCAmelCase	:     Optional[int]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     Union[str, Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	split=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'  ,	[str, list]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			text_path
 elif issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Optional[Any]						 =			[text_path]
 UpperCAmelCase	:     List[Any]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Union[str, Any]						 =			{'text': 'string'}
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=("train",)     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 for split in splits:
  UpperCAmelCase	:     Union[str, Any]						 =			dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     int						 =			TextDatasetReader({'train': text_path}  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     Union[str, Any]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader({'train': text_path}  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if split:
  UpperCAmelCase	:     int						 =			{split: text_path}
 else:
  UpperCAmelCase	:     int						 =			'train'
  UpperCAmelCase	:     Any						 =			{'train': text_path, 'test': text_path}
 UpperCAmelCase	:     Dict						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Any						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_  ,	splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )




