
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard





class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{
								    '''model_details''': {
								        '''Organization''': '''testing''',
								        '''Model date''': '''today''',
								        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
								        '''Architecture''': '''Convolutional Neural Network.''',
								    },
								    '''metrics''': '''BLEU and ROUGE-1''',
								    '''evaluation_data''': {
								        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
								        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
								    },
								    '''training_data''': {
								        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
								        '''Preprocessing''': (
								            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
								            ''' https://arxiv.org/pdf/1810.03993.pdf'''
								        ),
								    },
								    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
								}
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			ModelCard.from_dict(self.inputs_dict    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''model_details'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''intended_use'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''factors'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''metrics'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''evaluation_data'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''training_data'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''quantitative_analyses'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''ethical_considerations'''    )    )
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,	'''caveats_and_recommendations'''    )    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			ModelCard.from_dict(self.inputs_dict    )
								lowerCAmelCase__ :    List[str]				      =			json.loads(modelcard.to_json_string()    )
								for key, value in self.inputs_dict.items():
											self.assertEqual(obj[key] ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			ModelCard.from_dict(self.inputs_dict    )

								with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase__ :    Tuple				      =			os.path.join(SCREAMING_SNAKE_CASE__ ,	'''modelcard.json'''    )
											model_card_first.to_json_file(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			ModelCard.from_json_file(SCREAMING_SNAKE_CASE__    )

								self.assertEqual(model_card_second.to_dict() ,	model_card_first.to_dict()    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			ModelCard.from_dict(self.inputs_dict    )

								with tempfile.TemporaryDirectory() as tmpdirname:
											model_card_first.save_pretrained(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Optional[int]				      =			ModelCard.from_pretrained(SCREAMING_SNAKE_CASE__    )

								self.assertEqual(model_card_second.to_dict() ,	model_card_first.to_dict()    )





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()



