
import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def     a						(							snake_case__: str ):


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


__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 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Dict	)	->					List[Any]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_					  = Path(SCREAMING_SNAKE_CASE_	) / '''README.md'''
									with open(SCREAMING_SNAKE_CASE_	,							'''w+'''	) as readme_file:
													readme_file.write(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE_	)
									self.assertDictEqual(SCREAMING_SNAKE_CASE_	,							{'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

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

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




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

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

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






from typing import TYPE_CHECKING

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


__a        = {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DonutSwinModel',
						    'DonutSwinPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DonutFeatureExtractor']
						__a        = ['DonutImageProcessor']


if TYPE_CHECKING:
						from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
						from .processing_donut import DonutProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_donut_swin import (
												    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DonutSwinModel,
												    DonutSwinPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_donut import DonutFeatureExtractor
												from .image_processing_donut import DonutImageProcessor

else:
						import sys

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




