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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						NDArray[floataa]:
     a      ,				a	:     Dict						      =    np.shape(lowerCAmelCase_							)

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

          a	:     str						      =    (vector[row] - total) / coefficients[row, row]

     return x







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						NDArray[floataa]:
     a      ,				a	:     Tuple						      =    np.shape(lowerCAmelCase_							)
     if rows != columns:
          return np.array(()    ,   dtype=lowerCAmelCase_							)

     # augmented matrix
     a	:     NDArray[floataa]						      =    np.concatenate((coefficients, vector)    ,   axis=1							)
     a	:     Optional[int]						      =    augmented_mat.astype('''float64'''							)

     # scale the matrix leaving it triangular
     for row in range(rows - 1							):
          a	:     Dict						      =    augmented_mat[row, row]
          for col in range(row + 1    ,   lowerCAmelCase_							):
               a	:     List[str]						      =    augmented_mat[col, row] / pivot
               augmented_mat[col, :] -= factor * augmented_mat[row, :]

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

     return x


if __name__ == "__main__":
  import doctest

  doctest.testmod()
import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     a	:     Optional[Any]						      =    min(re.search('''^ +'''    ,   lowerCAmelCase_							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase_							)]							)


UpperCAmelCase				 =   """\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

UpperCAmelCase				 =   """\
---
---
# Begin of markdown

Some cool dataset card
"""


UpperCAmelCase				 =   """\
# Begin of markdown

Some cool dataset card
"""






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def _lowercase(     self     )						->List[str]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             a	:     List[str]						      =    Path(lowerCAmelCase_     ) / '''README.md'''
             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     Dict						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[str]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[Any]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertEqual(lowerCAmelCase_				,  {}     )


   def _lowercase(     self     )						->Optional[Any]:
        a	:     Tuple						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Dict						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''     )
        with self.assertRaises(lowerCAmelCase_     ):
             DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Optional[int]						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )