
"""simple docstring"""






import unittest

import numpy as np






def 						_snake_case       (	lowercase__		:  np.ndarray      ,     lowercase__		:  np.ndarray      ,     lowercase__		:  np.ndarray      ,     lowercase__		:  np.ndarray | None = None      ,     )  ->				np.ndarray:


			'''simple docstring'''





			lowerCAmelCase_    :Any			     =  np.shape(lowercase__		)
			lowerCAmelCase_    :int			     =  np.shape(lowercase__		)
			lowerCAmelCase_    :List[str]			     =  np.shape(lowercase__		)

			if shape_a[0] != shape_b[0]:
						lowerCAmelCase_    :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]:
						lowerCAmelCase_    :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(lowercase__		)

			lowerCAmelCase_    :Optional[int]			     =  pseudo_inv
			if a_inv is None:
						try:
									lowerCAmelCase_    :Optional[Any]			     =  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 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     None:
						lowerCAmelCase_    :Any			     =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
						lowerCAmelCase_    :int			     =  np.array([[0, 3], [3, 0], [2, 3]]       )
						lowerCAmelCase_    :List[str]			     =  np.array([[2, 1], [6, 3]]       )

						lowerCAmelCase_    :Optional[Any]			     =  schur_complement(__A       ,   __A       ,   __A       )

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

						lowerCAmelCase_    :int			     =  np.linalg.det(__A       )
						lowerCAmelCase_    :Union[str, Any]			     =  np.linalg.det(__A       )
						lowerCAmelCase_    :List[Any]			     =  np.linalg.det(__A       )

						self.assertAlmostEqual(__A       ,   det_a * det_s       )






			def  __lowerCAmelCase (					self       )			->     None:
						lowerCAmelCase_    :str			     =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
						lowerCAmelCase_    :Dict			     =  np.array([[0, 3], [3, 0], [2, 3]]       )
						lowerCAmelCase_    :int			     =  np.array([[2, 1], [6, 3]]       )

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






			def  __lowerCAmelCase (					self       )			->     None:
						lowerCAmelCase_    :Optional[int]			     =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
						lowerCAmelCase_    :str			     =  np.array([[0, 3], [3, 0], [2, 3]]       )
						lowerCAmelCase_    :int			     =  np.array([[2, 1, 3], [6, 3, 5]]       )

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'camembert'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_    ,   **UpperCamelCase_							)

							UpperCamelCase__   :int												= vocab_size
							UpperCamelCase__   :Optional[int]												= hidden_size
							UpperCamelCase__   :Optional[int]												= num_hidden_layers
							UpperCamelCase__   :List[Any]												= num_attention_heads
							UpperCamelCase__   :Union[str, Any]												= hidden_act
							UpperCamelCase__   :List[Any]												= intermediate_size
							UpperCamelCase__   :int												= hidden_dropout_prob
							UpperCamelCase__   :Tuple												= attention_probs_dropout_prob
							UpperCamelCase__   :Union[str, Any]												= max_position_embeddings
							UpperCamelCase__   :Tuple												= type_vocab_size
							UpperCamelCase__   :int												= initializer_range
							UpperCamelCase__   :List[str]												= layer_norm_eps
							UpperCamelCase__   :int												= position_embedding_type
							UpperCamelCase__   :Any												= use_cache
							UpperCamelCase__   :Any												= classifier_dropout



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__   :List[str]												= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__   :Tuple												= {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)