"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__    				=					{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
     from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
     from .tokenization_fsmt import FSMTTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



"""simple docstring"""



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def   _lowerCamelCase ( UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		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__					)]					)


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

__magic_name__    				=					"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__magic_name__    				=					"\\n# Begin of markdown\n\nSome cool dataset card\n"




class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Dict:




    '''simple docstring'''






    with tempfile.TemporaryDirectory() as tmp_dir:
       a__     				=		Path(_snake_case				) / 'README.md'
       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}				)

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertDictEqual(_snake_case					,   {}				)

       with open(_snake_case					,   'w+'				) as readme_file:
          readme_file.write(_snake_case				)
       a__     				=		DatasetMetadata.from_readme(_snake_case				)
       self.assertEqual(_snake_case					,   {}				)



 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		_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(_snake_case				)

    a__     				=		_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(_snake_case				):
       DatasetMetadata.from_yaml_string(_snake_case				)

    a__     				=		_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(_snake_case				)


