

"""simple docstring"""





import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def     _A    (    UpperCamelCase_	:			str)      ->		str:
   '''simple docstring'''


   __lowercase								=		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_)])


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

_a					 =				'\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_a					 =				'\\n# Begin of markdown\n\nSome cool dataset card\n'




class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    with tempfile.TemporaryDirectory() as tmp_dir:
       __lowercase								=		Path(UpperCAmelCase__      ) / "README.md"
       with open(UpperCAmelCase__,   "w+"      ) as readme_file:
          readme_file.write(UpperCAmelCase__      )
       __lowercase								=		DatasetMetadata.from_readme(UpperCAmelCase__      )
       self.assertDictEqual(UpperCAmelCase__,   {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}      )

       with open(UpperCAmelCase__,   "w+"      ) as readme_file:
          readme_file.write(UpperCAmelCase__      )
       __lowercase								=		DatasetMetadata.from_readme(UpperCAmelCase__      )
       self.assertDictEqual(UpperCAmelCase__,   {}      )

       with open(UpperCAmelCase__,   "w+"      ) as readme_file:
          readme_file.write(UpperCAmelCase__      )
       __lowercase								=		DatasetMetadata.from_readme(UpperCAmelCase__      )
       self.assertEqual(UpperCAmelCase__,   {}      )



 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		_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(UpperCAmelCase__      )

    __lowercase								=		_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(UpperCAmelCase__      ):
       DatasetMetadata.from_yaml_string(UpperCAmelCase__      )

    __lowercase								=		_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(UpperCAmelCase__      )







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			str)      ->		int:
   '''simple docstring'''


   __lowercase								=		0
   for plain_chr in plain_text:
      __lowercase								=		(
          ord(UpperCamelCase_) + (hash_value << 6) + (hash_value << 16) - hash_value
      )
   return hash_value




