'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase		:				Any	       =		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Union[str, Any]	       =		["DistilBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DistilBertForMaskedLM",
						    "DistilBertForMultipleChoice",
						    "DistilBertForQuestionAnswering",
						    "DistilBertForSequenceClassification",
						    "DistilBertForTokenClassification",
						    "DistilBertModel",
						    "DistilBertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFDistilBertForMaskedLM",
						    "TFDistilBertForMultipleChoice",
						    "TFDistilBertForQuestionAnswering",
						    "TFDistilBertForSequenceClassification",
						    "TFDistilBertForTokenClassification",
						    "TFDistilBertMainLayer",
						    "TFDistilBertModel",
						    "TFDistilBertPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Tuple	       =		[
						    "FlaxDistilBertForMaskedLM",
						    "FlaxDistilBertForMultipleChoice",
						    "FlaxDistilBertForQuestionAnswering",
						    "FlaxDistilBertForSequenceClassification",
						    "FlaxDistilBertForTokenClassification",
						    "FlaxDistilBertModel",
						    "FlaxDistilBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_distilbert import (
						    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DistilBertConfig,
						    DistilBertOnnxConfig,
						)
						from .tokenization_distilbert import DistilBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_distilbert_fast import DistilBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_distilbert import (
												    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DistilBertForMaskedLM,
												    DistilBertForMultipleChoice,
												    DistilBertForQuestionAnswering,
												    DistilBertForSequenceClassification,
												    DistilBertForTokenClassification,
												    DistilBertModel,
												    DistilBertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_distilbert import (
												    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDistilBertForMaskedLM,
												    TFDistilBertForMultipleChoice,
												    TFDistilBertForQuestionAnswering,
												    TFDistilBertForSequenceClassification,
												    TFDistilBertForTokenClassification,
												    TFDistilBertMainLayer,
												    TFDistilBertModel,
												    TFDistilBertPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_distilbert import (
												    FlaxDistilBertForMaskedLM,
												    FlaxDistilBertForMultipleChoice,
												    FlaxDistilBertForQuestionAnswering,
												    FlaxDistilBertForSequenceClassification,
												    FlaxDistilBertForTokenClassification,
												    FlaxDistilBertModel,
												    FlaxDistilBertPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				List[str]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''simple docstring'''




import unittest

import numpy as np




def 							lowerCamelCase			(   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray | None = None   ,   )    ->   np.ndarray:
							lowercase_      :    List[Any]       		= np.shape(UpperCAmelCase__				)
							lowercase_      :    Dict       		= np.shape(UpperCAmelCase__				)
							lowercase_      :    int       		= np.shape(UpperCAmelCase__				)

							if shape_a[0] != shape_b[0]:
														lowercase_      :    Optional[int]       		= (
														    """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]:
														lowercase_      :    Optional[Any]       		= (
														    """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__				)

							lowercase_      :    Any       		= pseudo_inv
							if a_inv is None:
														try:
																					lowercase_      :    List[str]       		= 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 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Tuple       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    int       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    Dict       		= np.array([[2, 1], [6, 3]]					)

										lowercase_      :    Union[str, Any]       		= schur_complement(lowercase_       ,    lowercase_       ,    lowercase_					)

										lowercase_      :    List[Any]       		= np.block([[a, b], [b.T, c]]					)

										lowercase_      :    Optional[int]       		= np.linalg.det(lowercase_					)
										lowercase_      :    int       		= np.linalg.det(lowercase_					)
										lowercase_      :    int       		= np.linalg.det(lowercase_					)

										self.assertAlmostEqual(lowercase_       ,    det_a * det_s					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    int       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    Optional[Any]       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    Union[str, Any]       		= np.array([[2, 1], [6, 3]]					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    List[Any]       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    List[Any]       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    str       		= 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()





