



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      min(re.search('''^ +'''		,       lowercase__    ).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(lowercase__    )]    )


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

A__     :      List[str]             =			'\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


A__     :      Optional[int]             =			'\\n# Begin of markdown\n\nSome cool dataset card\n'




class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''







								with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase__       			=      Path(lowerCamelCase  ) / "README.md"
												with open(lowerCamelCase,  '''w+'''  ) as readme_file:
																readme_file.write(lowerCamelCase  )
												lowercase__       			=      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__       			=      DatasetMetadata.from_readme(lowerCamelCase  )
												self.assertDictEqual(lowerCamelCase,  {}  )

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




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								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(lowerCamelCase  )

								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(lowerCamelCase  ):
												DatasetMetadata.from_yaml_string(lowerCamelCase  )

								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(lowerCamelCase  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




