





import unittest

import numpy as np


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__ = None							,):
		lowerCamelCase_	:     Optional[int] 						=      np.shape(lowerCAmelCase__						)
		lowerCamelCase_	:     int 						=      np.shape(lowerCAmelCase__						)
		lowerCamelCase_	:     Dict 						=      np.shape(lowerCAmelCase__						)

		if shape_a[0] != shape_b[0]:
				lowerCamelCase_	:     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(lowerCAmelCase__						)

		if shape_b[1] != shape_c[1]:
				lowerCamelCase_	:     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(lowerCAmelCase__						)

		lowerCamelCase_	:     Tuple 						=      pseudo_inv
		if a_inv is None:
				try:
						lowerCamelCase_	:     Dict 						=      np.linalg.inv(lowerCAmelCase__						)
				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 						UpperCamelCase_  (  unittest.TestCase							):



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     List[str] 						=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
							lowerCamelCase_	:     List[Any] 						=      np.array([[0, 3], [3, 0], [2, 3]]						)
							lowerCamelCase_	:     List[str] 						=      np.array([[2, 1], [6, 3]]						)

							lowerCamelCase_	:     int 						=      schur_complement(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)

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

							lowerCamelCase_	:     Dict 						=      np.linalg.det(lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      np.linalg.det(lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      np.linalg.det(lowerCamelCase						)

							self.assertAlmostEqual(lowerCamelCase       ,      det_a * det_s						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Optional[Any] 						=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
							lowerCamelCase_	:     List[Any] 						=      np.array([[0, 3], [3, 0], [2, 3]]						)
							lowerCamelCase_	:     List[str] 						=      np.array([[2, 1], [6, 3]]						)

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





					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
							lowerCamelCase_	:     int 						=      np.array([[0, 3], [3, 0], [2, 3]]						)
							lowerCamelCase_	:     int 						=      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()







from typing import TYPE_CHECKING

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


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
