
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest





@custom_tokenizers
class 						lowercase       (  __UpperCamelCase					):
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return True
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''    )
								lowerCAmelCase__ :    Dict				      =			'''Hugging Face大法好，谁用谁知道。'''
								lowerCAmelCase__ :    Tuple				      =			'''Hugging Face大法好,谁用谁知道。<unk>'''
								lowerCAmelCase__ :    str				      =			'''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

								lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Any				      =			tokens + [tokenizer.unk_token]

								lowerCAmelCase__ :    Optional[Any]				      =			[13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Any				      =			tokenizer.decode(SCREAMING_SNAKE_CASE__    )
								self.assertEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



