




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


a													=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}




class       _A				(		__lowercase     ):
			__a					    =       """albert"""






			def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0000						,		_SCREAMING_SNAKE_CASE=128						,		_SCREAMING_SNAKE_CASE=4096						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE=1_6384						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE="gelu_new"						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE="absolute"						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=3						,		**_SCREAMING_SNAKE_CASE						,		):
									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		bos_token_id=_SCREAMING_SNAKE_CASE						,		eos_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

									_UpperCAmelCase      =			vocab_size
									_UpperCAmelCase      =			embedding_size
									_UpperCAmelCase      =			hidden_size
									_UpperCAmelCase      =			num_hidden_layers
									_UpperCAmelCase      =			num_hidden_groups
									_UpperCAmelCase      =			num_attention_heads
									_UpperCAmelCase      =			inner_group_num
									_UpperCAmelCase      =			hidden_act
									_UpperCAmelCase      =			intermediate_size
									_UpperCAmelCase      =			hidden_dropout_prob
									_UpperCAmelCase      =			attention_probs_dropout_prob
									_UpperCAmelCase      =			max_position_embeddings
									_UpperCAmelCase      =			type_vocab_size
									_UpperCAmelCase      =			initializer_range
									_UpperCAmelCase      =			layer_norm_eps
									_UpperCAmelCase      =			classifier_dropout_prob
									_UpperCAmelCase      =			position_embedding_type




class       _A				(		__lowercase     ):






			@property
			def 				UpperCAmelCase (    self			):
									if self.task == "multiple-choice":
															_UpperCAmelCase      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
															_UpperCAmelCase      =			{0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""input_ids""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									        ("""token_type_ids""", dynamic_axis),
									    ]			)





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												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       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()