
'''simple docstring'''







from __future__ import annotations

import numpy as np



def 			_a		(	_SCREAMING_SNAKE_CASE		:   np.ndarray  ):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= np.shape(_SCREAMING_SNAKE_CASE  )
						if rows != columns:
												_SCREAMING_SNAKE_CASE				= (
												    "'table' has to be of square shaped array but got a "
												    F'{rows}x{columns} array:\n{table}'
												)
												raise ValueError(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= np.zeros((rows, columns)  )
						_SCREAMING_SNAKE_CASE				= np.zeros((rows, columns)  )
						for i in range(_SCREAMING_SNAKE_CASE  ):
												for j in range(_SCREAMING_SNAKE_CASE  ):
																		_SCREAMING_SNAKE_CASE				= sum(lower[i][k] * upper[k][j] for k in range(_SCREAMING_SNAKE_CASE  )  )
																		if upper[j][j] == 0:
																								raise ArithmeticError("No LU decomposition exists"  )
																		_SCREAMING_SNAKE_CASE				= (table[i][j] - total) / upper[j][j]
												_SCREAMING_SNAKE_CASE				= 1
												for j in range(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ):
																		_SCREAMING_SNAKE_CASE				= sum(lower[i][k] * upper[k][j] for k in range(_SCREAMING_SNAKE_CASE  )  )
																		_SCREAMING_SNAKE_CASE				= table[i][j] - total
						return lower, upper


if __name__ == "__main__":
						import doctest

						doctest.testmod()

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]