






'''simple docstring'''







import unittest

import numpy as np





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase = None				,			)      ->       np.ndarray:
							lowerCamelCase__					:	Tuple         =       np.shape(UpperCamelCase      )
							lowerCamelCase__					:	Optional[int]         =       np.shape(UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       np.shape(UpperCamelCase      )

							if shape_a[0] != shape_b[0]:
														lowerCamelCase__					:	List[str]         =       (
														    """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]:
														lowerCamelCase__					:	Tuple         =       (
														    """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      )

							lowerCamelCase__					:	Optional[Any]         =       pseudo_inv
							if a_inv is None:
														try:
																					lowerCamelCase__					:	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 _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[int]         =       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
														lowerCamelCase__					:	List[Any]         =       np.array([[0, 3], [3, 0], [2, 3]]     )
														lowerCamelCase__					:	Optional[Any]         =       np.array([[2, 1], [6, 3]]     )

														lowerCamelCase__					:	Dict         =       schur_complement(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

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

														lowerCamelCase__					:	List[Any]         =       np.linalg.det(UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       np.linalg.det(UpperCamelCase__     )
														lowerCamelCase__					:	Optional[Any]         =       np.linalg.det(UpperCamelCase__     )

														self.assertAlmostEqual(UpperCamelCase__			,							det_a * det_s     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	List[Any]         =       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
														lowerCamelCase__					:	Tuple         =       np.array([[0, 3], [3, 0], [2, 3]]     )
														lowerCamelCase__					:	str         =       np.array([[2, 1], [6, 3]]     )

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Dict         =       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, 3], [6, 3, 5]]     )

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						_lowercase						: int						=						None

_lowercase						: Union[str, Any]						=						logging.get_logger(__name__)
_lowercase						: Tuple						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase						: Dict						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase						: int						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						: Tuple						=						"▁"





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       VOCAB_FILES_NAMES
			UpperCamelCase__				=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__				=       AlbertTokenizer







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Union[str, Any]=None				,			lowercase_				:  str=None				,			lowercase_				:  Any=True				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  List[str]=False				,			lowercase_				:  Optional[int]="[CLS]"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<unk>"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<pad>"				,			lowercase_				:  Tuple="[CLS]"				,			lowercase_				:  Dict="[MASK]"				,			**lowercase_				:  Optional[int]				,			):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase_					:			Tuple										=       (
										    AddedToken(lowercase_				,			lstrip=lowercase_				,			rstrip=lowercase_				,			normalized=lowercase_    )
										    if isinstance(lowercase_				,			lowercase_    )
										    else mask_token
										)

										super().__init__(
										    lowercase_				,			tokenizer_file=lowercase_				,			do_lower_case=lowercase_				,			remove_space=lowercase_				,			keep_accents=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			unk_token=lowercase_				,			sep_token=lowercase_				,			pad_token=lowercase_				,			cls_token=lowercase_				,			mask_token=lowercase_				,			**lowercase_				,			)

										lowercase_					:			Optional[int]										=       do_lower_case
										lowercase_					:			Any										=       remove_space
										lowercase_					:			Dict										=       keep_accents
										lowercase_					:			Optional[int]										=       vocab_file
										lowercase_					:			Any										=       False if not self.vocab_file else True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Tuple										=       [self.sep_token_id]
										lowercase_					:			str										=       [self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Union[str, Any]										=       [self.sep_token_id]
										lowercase_					:			int										=       [self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep    ) * [0]
										return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[str] = None    ):
										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																	    """tokenizer."""    )

										if not os.path.isdir(lowercase_    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
										lowercase_					:			Optional[Any]										=       os.path.join(
										    lowercase_				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

										if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase_    ):
																	copyfile(self.vocab_file				,			lowercase_    )

										return (out_vocab_file,)





