


# 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


snake_case__			 =						{
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case__			 =						['''VivitImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case__			 =						[
		    '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VivitModel''',
		    '''VivitPreTrainedModel''',
		    '''VivitForVideoClassification''',
		]


if TYPE_CHECKING:
		from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_vivit import VivitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vivit import (
				    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VivitForVideoClassification,
				    VivitModel,
				    VivitPreTrainedModel,
				)


else:
		import sys

		snake_case__			 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






import numpy as np


def 				lowerCamelCase__	(      a	:   np.ndarray	,  a	:   np.ndarray	,  a	:   float = 1e-12	,  a	:   int = 100	,  )    ->	tuple[float, np.ndarray]:





			"""simple docstring"""
			assert np.shape(a				)[0] == np.shape(a				)[1]
			# Ensure proper dimensionality.
			assert np.shape(a				)[0] == np.shape(a				)[0]
			# Ensure inputs are either both complex or both real
			assert np.iscomplexobj(a				) == np.iscomplexobj(a				)
			a__ :List[str]						  =    np.iscomplexobj(a				)
			if is_complex:
						# Ensure complex input_matrix is Hermitian
						assert np.array_equal(a	,  input_matrix.conj().T				)

			# Set convergence to False. Will define convergence when we exceed max_iterations
			# or when we have small changes from one iteration to next.

			a__ :List[str]						  =    False
			a__ :str						  =    0
			a__ :Dict						  =    0
			a__ :Tuple						  =    1e12

			while not convergence:
						# Multiple matrix by the vector.
						a__ :Tuple						  =    np.dot(a	,  a				)
						# Normalize the resulting output vector.
						a__ :List[Any]						  =    w / np.linalg.norm(a				)
						# Find rayleigh quotient
						# (faster than usual b/c we know vector is normalized already)
						a__ :List[str]						  =    vector.conj().T if is_complex else vector.T
						a__ :Any						  =    np.dot(a	,  np.dot(a	,  a				)				)

						# Check convergence.
						a__ :Optional[Any]						  =    np.abs(lambda_ - lambda_previous				) / lambda_
						iterations += 1

						if error <= error_tol or iterations >= max_iterations:
									a__ :List[str]						  =    True

						a__ :Optional[int]						  =    lambda_

			if is_complex:
						a__ :Union[str, Any]						  =    np.real(lambda_				)

			return lambda_, vector


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :List[str]						  =    np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
			a__ :Dict						  =    np.array([41, 4, 20]				)
			a__ :Union[str, Any]						  =    real_input_matrix.astype(np.complexaaa				)
			a__ :Optional[Any]						  =    np.triu(1j * complex_input_matrix	,  1				)
			complex_input_matrix += imag_matrix
			complex_input_matrix += -1 * imag_matrix.T
			a__ :Union[str, Any]						  =    np.array([41, 4, 20]				).astype(np.complexaaa				)

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									a__ :Union[str, Any]						  =    real_input_matrix
									a__ :int						  =    real_vector
						elif problem_type == "complex":
									a__ :Optional[Any]						  =    complex_input_matrix
									a__ :List[Any]						  =    complex_vector

						# Our implementation.
						a__  ,  a__ :Any						  =    power_iteration(a	,  a				)

						# Numpy implementation.

						# Get eigenvalues and eigenvectors using built-in numpy
						# eigh (eigh used for symmetric or hermetian matrices).
						a__  ,  a__ :int						  =    np.linalg.eigh(a				)
						# Last eigenvalue is the maximum one.
						a__ :int						  =    eigen_values[-1]
						# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
						a__ :Optional[Any]						  =    eigen_vectors[:, -1]

						# Check our implementation and numpy gives close answers.
						assert np.abs(eigen_value - eigen_value_max				) <= 1e-6
						# Take absolute values element wise of each eigenvector.
						# as they are only unique to a minus sign.
						assert np.linalg.norm(np.abs(a				) - np.abs(a				)				) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


