

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    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__						)]						)


lowerCAmelCase__             =		"""\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

lowerCAmelCase__             =		"""\
---
---
# Begin of markdown

Some cool dataset card
"""


lowerCAmelCase__             =		"""\
# Begin of markdown

Some cool dataset card
"""







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			with tempfile.TemporaryDirectory() as tmp_dir:
				_UpperCamelCase :		Optional[Any]								=    Path(_A) / 'README.md'
				with open(_A						,							'w+') as readme_file:
					readme_file.write(_A)
				_UpperCamelCase :		List[str]								=    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)
				_UpperCamelCase :		List[Any]								=    DatasetMetadata.from_readme(_A)
				self.assertDictEqual(_A						,							{})

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






		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    _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)

			_UpperCamelCase :		Union[str, Any]								=    _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)

			_UpperCamelCase :		Dict								=    _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)







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCamelCase   					=  2 << 2_0  # 2mb
lowerCamelCase   					=  get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				self._options.get_all_options().get('runner'				)
												return runner in [None, "DirectRunner", "PortableRunner"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if fs.exists(UpperCAmelCase__   ):
										if force_upload:
															logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.create(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'rb'   ) as local_file:
															__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )
															while chunk:
																				remote_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if os.path.exists(UpperCAmelCase__   ):
										if force_download:
															logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.open(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'wb'   ) as local_file:
															__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )
															while chunk:
																				local_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )






