





import unittest

import numpy as np


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray | None = None   ,    )   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      str      =		np.shape(_lowercase			)
		lowercase__					:      int      =		np.shape(_lowercase			)
		lowercase__					:      Dict      =		np.shape(_lowercase			)

		if shape_a[0] != shape_b[0]:
				lowercase__					:      Tuple      =		(
				    '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(_lowercase			)

		if shape_b[1] != shape_c[1]:
				lowercase__					:      List[str]      =		(
				    '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(_lowercase			)

		lowercase__					:      Any      =		pseudo_inv
		if a_inv is None:
				try:
						lowercase__					:      str      =		np.linalg.inv(_lowercase			)
				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       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Dict			):
			lowercase__					:      Tuple      =		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], [6, 3]]			)

			lowercase__					:      List[Any]      =		schur_complement(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

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

			lowercase__					:      Dict      =		np.linalg.det(lowerCamelCase_			)
			lowercase__					:      Tuple      =		np.linalg.det(lowerCamelCase_			)
			lowercase__					:      int      =		np.linalg.det(lowerCamelCase_			)

			self.assertAlmostEqual(lowerCamelCase_,					det_a * det_s			)

	def    snake_case__(						self:						Any			):
			lowercase__					:      Any      =		np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
			lowercase__					:      Tuple      =		np.array([[0, 3], [3, 0], [2, 3]]			)
			lowercase__					:      Tuple      =		np.array([[2, 1], [6, 3]]			)

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





	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Optional[int]      =		np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
			lowercase__					:      Optional[int]      =		np.array([[0, 3], [3, 0], [2, 3]]			)
			lowercase__					:      List[str]      =		np.array([[2, 1, 3], [6, 3, 5]]			)

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:	List[Any] = {
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = ["""EfficientNetImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = [
			    """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """EfficientNetForImageClassification""",
			    """EfficientNetModel""",
			    """EfficientNetPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_efficientnet import (
			    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    EfficientNetConfig,
			    EfficientNetOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_efficientnet import EfficientNetImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_efficientnet import (
						    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EfficientNetForImageClassification,
						    EfficientNetModel,
						    EfficientNetPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:	List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)


