



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def 						__lowerCamelCase       (      __magic_name__	:  str						):
							a__:       List[str]											=min(re.search("^ +"     , __magic_name__						).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(__magic_name__						)]						)


__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 	lowerCamelCase__ (		unittest.TestCase   ):


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

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

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







						def   _lowerCamelCase			( self						:  Any			):
													a__:       List[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(_a			)

													a__:       Tuple											=_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(_a			):
																				DatasetMetadata.from_yaml_string(_a			)

													a__:       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            - 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(_a			)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase						  =     {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     ['''SplinterTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     [
		    '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SplinterForQuestionAnswering''',
		    '''SplinterForPreTraining''',
		    '''SplinterLayer''',
		    '''SplinterModel''',
		    '''SplinterPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase						  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





