



"""simple docstring"""




import numpy as np


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[Any]:






      '''simple docstring'''


      lowercase  :      str					=   n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      lowercase  :      int					=   np.floor(_UpperCAmelCase				)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      lowercase  :      Optional[Any]					=   int(n_draws - floored.sum()				)
      if need_to_add > 0:
            lowercase  :      List[str]					=   continuous - floored
            lowercase  :      List[Any]					=   np.sort(np.unique(_UpperCAmelCase				)				)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  (lowercase    )  :      Optional[int]					=   np.where(remainder == value				)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  lowercase  :      Optional[int]					=   min(len(_UpperCAmelCase				)	,      _UpperCAmelCase				)
                  lowercase  :      List[str]					=   rng.choice(_UpperCAmelCase	,      size=_UpperCAmelCase	,      replace=_UpperCAmelCase				)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa				)





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase=10				)     ->			Optional[int]:






      '''simple docstring'''


      lowercase  :      List[str]					=   np.unique(_UpperCAmelCase	,      return_inverse=_UpperCAmelCase				)
      lowercase  :      Tuple					=   classes.shape[0]
      lowercase  :      int					=   np.bincount(_UpperCAmelCase				)
      if np.min(_UpperCAmelCase				) < 2:
            raise ValueError('Minimum class count error'				)
      if n_train < n_classes:
            raise ValueError(
                'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)				)
      if n_test < n_classes:
            raise ValueError(
                'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)				)
      lowercase  :      Union[str, Any]					=   np.split(np.argsort(_UpperCAmelCase	,      kind='mergesort'				)	,      np.cumsum(_UpperCAmelCase				)[:-1]				)
      for _ in range(_UpperCAmelCase				):
            lowercase  :      Union[str, Any]					=   approximate_mode(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)
            lowercase  :      Optional[Any]					=   class_counts - n_i
            lowercase  :      Union[str, Any]					=   approximate_mode(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)

            lowercase  :      List[str]					=   []
            lowercase  :      List[Any]					=   []

            for i in range(_UpperCAmelCase				):
                  lowercase  :      str					=   rng.permutation(class_counts[i]				)
                  lowercase  :      Union[str, Any]					=   class_indices[i].take(_UpperCAmelCase	,      mode='clip'				)
                  train.extend(perm_indices_class_i[: n_i[i]]				)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
            lowercase  :      Optional[int]					=   rng.permutation(_UpperCAmelCase				)
            lowercase  :      Any					=   rng.permutation(_UpperCAmelCase				)

            yield train, test





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).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(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   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			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






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

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

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