




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def       _a (		SCREAMING_SNAKE_CASE_				:							str	):
					__lowerCAmelCase							  =			min(re.search("^ +" , SCREAMING_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(SCREAMING_SNAKE_CASE_	)]	)


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      a__       (   unittest.TestCase ):






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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

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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			_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     )

											__lowerCAmelCase							  =			_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     )

											__lowerCAmelCase							  =			_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_torch_available, is_vision_available


UpperCamelCase__            =     {"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ViTHybridForImageClassification""",
					    """ViTHybridModel""",
					    """ViTHybridPreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_hybrid import (
										    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTHybridForImageClassification,
										    ViTHybridModel,
										    ViTHybridPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
					import sys

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




