





from collections import OrderedDict
from typing import Mapping

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


A   : int			=				logging.get_logger(__name__)

A   : Any			=				{
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[int]          =   '''xlm'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}






			def __init__(      self       :   List[str]							,     __lowerCAmelCase       :   str=3_01_45							,     __lowerCAmelCase       :   Optional[int]=20_48							,     __lowerCAmelCase       :   List[Any]=12							,     __lowerCAmelCase       :   str=16							,     __lowerCAmelCase       :   List[Any]=0.1							,     __lowerCAmelCase       :   int=0.1							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   Optional[Any]=False							,     __lowerCAmelCase       :   int=False							,     __lowerCAmelCase       :   List[Any]=False							,     __lowerCAmelCase       :   Optional[Any]=1							,     __lowerCAmelCase       :   Dict=True							,     __lowerCAmelCase       :   Any=5_12							,     __lowerCAmelCase       :   Optional[int]=20_48**-0.5							,     __lowerCAmelCase       :   Optional[int]=1e-12							,     __lowerCAmelCase       :   Optional[int]=0.0_2							,     __lowerCAmelCase       :   Any=0							,     __lowerCAmelCase       :   Tuple=1							,     __lowerCAmelCase       :   List[str]=2							,     __lowerCAmelCase       :   Union[str, Any]=3							,     __lowerCAmelCase       :   Any=5							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Optional[Any]="first"							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   Dict=None							,     __lowerCAmelCase       :   str=True							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   int=5							,     __lowerCAmelCase       :   Optional[int]=5							,     __lowerCAmelCase       :   Optional[Any]=0							,     __lowerCAmelCase       :   List[str]=0							,     __lowerCAmelCase       :   Optional[int]=2							,     __lowerCAmelCase       :   str=0							,     **__lowerCAmelCase       :   int							,     )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       emb_dim
					A__  				=       n_layers
					A__  				=       n_heads
					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       gelu_activation
					A__  				=       sinusoidal_embeddings
					A__  				=       causal
					A__  				=       asm
					A__  				=       n_langs
					A__  				=       use_lang_emb
					A__  				=       layer_norm_eps
					A__  				=       bos_index
					A__  				=       eos_index
					A__  				=       pad_index
					A__  				=       unk_index
					A__  				=       mask_index
					A__  				=       is_encoder
					A__  				=       max_position_embeddings
					A__  				=       embed_init_std
					A__  				=       init_std
					A__  				=       summary_type
					A__  				=       summary_use_proj
					A__  				=       summary_activation
					A__  				=       summary_proj_to_labels
					A__  				=       summary_first_dropout
					A__  				=       start_n_top
					A__  				=       end_n_top
					A__  				=       mask_token_id
					A__  				=       lang_id

					if "n_words" in kwargs:
							A__  				=       kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase							,     bos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase       )


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   Optional[int]       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__  				=       {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__  				=       {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        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """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(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """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(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				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   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

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

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

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







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       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()



