




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	str:


      '''simple docstring'''




      lowerCamelCase__					     = min(re.search('^ +'   , lowerCamelCase  ).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(lowerCamelCase  )]  )


__SCREAMING_SNAKE_CASE			:      Optional[Any]      			="""\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

__SCREAMING_SNAKE_CASE			:      str      			="""\
---
---
# Begin of markdown

Some cool dataset card
"""


__SCREAMING_SNAKE_CASE			:      List[str]      			="""\
# Begin of markdown

Some cool dataset card
"""






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Union[str, Any]	) ->				List[str]:
           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        __snake_case      (			self     :      Dict	) ->				List[str]:
           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__	)





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]