from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:				Optional[Any]	       =		logging.get_logger(__name__)

_lowercase		:				Optional[Any]	       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''vivit'''







			def __init__(			self       :						List[str]       ,    lowercase_       :						Optional[Any]=224       ,    lowercase_       :						List[str]=32       ,    lowercase_       :						Any=[2, 16, 16]       ,    lowercase_       :						List[str]=3       ,    lowercase_       :						List[Any]=768       ,    lowercase_       :						str=12       ,    lowercase_       :						Union[str, Any]=12       ,    lowercase_       :						Tuple=3072       ,    lowercase_       :						Tuple="gelu_fast"       ,    lowercase_       :						List[str]=0.0       ,    lowercase_       :						Optional[Any]=0.0       ,    lowercase_       :						Optional[int]=0.02       ,    lowercase_       :						int=1E-06       ,    lowercase_       :						Union[str, Any]=True       ,    **lowercase_       :						Optional[int]       ,    ):
										lowercase_      :    Tuple       		= hidden_size
										lowercase_      :    Any       		= num_hidden_layers
										lowercase_      :    Any       		= num_attention_heads
										lowercase_      :    int       		= intermediate_size
										lowercase_      :    Optional[Any]       		= hidden_act
										lowercase_      :    Optional[Any]       		= hidden_dropout_prob
										lowercase_      :    Optional[Any]       		= attention_probs_dropout_prob
										lowercase_      :    Dict       		= initializer_range
										lowercase_      :    str       		= layer_norm_eps

										lowercase_      :    Union[str, Any]       		= image_size
										lowercase_      :    Tuple       		= num_frames
										lowercase_      :    Tuple       		= tubelet_size
										lowercase_      :    Union[str, Any]       		= num_channels
										lowercase_      :    str       		= qkv_bias

										super().__init__(**lowercase_					)






'''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()





