






'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class      __SCREAMING_SNAKE_CASE      (			yaml.SafeLoader				):




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Any      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=[self.constructed_objects[key_node] for key_node, _ in node.value]
       SCREAMING_SNAKE_CASE__     :				Any									=[tuple(__lowercase      ) if isinstance(__lowercase					,      __lowercase      ) else key for key in keys]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Counter(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=[key for key in counter if counter[key] > 1]
       if duplicate_keys:
             raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}"      )




 def      __magic_name__	(      self	:							int					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[Any]=False      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Dict									=super().construct_mapping(__lowercase					,      deep=__lowercase      )
       self._check_no_duplicates_on_constructed_node(__lowercase      )
       return mapping





def  _a(  UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE__     :				str									=full_content[1:].index('''---'''							) + 1
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''\n'''.join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(UpperCamelCase__							)






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 # class attributes
 snake_case_  							=							{"""train_eval_index"""}  # train-eval-index in the YAML metadata




 @classmethod
 def      __magic_name__	(      cls	:							str					,      __lowercase	:							Path      )	->						"DatasetMetadata":
       with open(__lowercase					,      encoding='''utf-8'''      ) as readme_file:
             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=_split_yaml_from_readme(readme_file.read()      )
       if yaml_string is not None:
             return cls.from_yaml_string(__lowercase      )
       else:
             return cls()




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							Path      )	->						Optional[Any]:
       if path.exists():
             with open(__lowercase					,      encoding='''utf-8'''      ) as readme_file:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=readme_file.read()
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=None
       SCREAMING_SNAKE_CASE__     :				Dict									=self._to_readme(__lowercase      )
       with open(__lowercase					,      '''w'''					,      encoding='''utf-8'''      ) as readme_file:
             readme_file.write(__lowercase      )




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Optional[str] = None      )	->						str:
       if readme_content is not None:
             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=_split_yaml_from_readme(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''---\n''' + self.to_yaml_string() + '''---\n''' + content
       else:
             SCREAMING_SNAKE_CASE__     :				Dict									='''---\n''' + self.to_yaml_string() + '''---\n'''
       return full_content




 @classmethod
 def      __magic_name__	(      cls	:							Any					,      __lowercase	:							str      )	->						"DatasetMetadata":
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=yaml.load(__lowercase					,      Loader=_NoDuplicateSafeLoader      ) or {}

       # Convert the YAML keys to DatasetMetadata fields
       SCREAMING_SNAKE_CASE__     :				Optional[int]									={
           (key.replace('''-'''					,      '''_'''      ) if key.replace('''-'''					,      '''_'''      ) in cls._FIELDS_WITH_DASHES else key): value
           for key, value in metadata_dict.items()
       }
       return cls(**__lowercase      )




 def      __magic_name__	(      self	:							Dict      )	->						str:
       return yaml.safe_dump(
           {
               (key.replace('''_'''					,      '''-'''      ) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           }					,      sort_keys=__lowercase					,      allow_unicode=__lowercase					,      encoding='''utf-8'''					,      ).decode('''utf-8'''      )


a_					= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
  from argparse import ArgumentParser

  a_					= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
  ap.add_argument('readme_filepath')
  a_					= ap.parse_args()

  a_					= Path(args.readme_filepath)
  a_					= DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)







'''simple docstring'''
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							JukeboxTokenizer
 snake_case_  							=							{
     """artist""": """Zac Brown Band""",
     """genres""": """Country""",
     """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
 }




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       import torch

       SCREAMING_SNAKE_CASE__     :				List[str]									=JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''      )
       SCREAMING_SNAKE_CASE__     :				str									=tokenizer(**self.metas      )['''input_ids''']
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				str									=[
           torch.tensor([[
               0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
               76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
               44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
               47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
               76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
               30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
               27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
               45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
               41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
               76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
               76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
               64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
               30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
               27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
               34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
               27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
               41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
               44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
               76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
               32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
               40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
               45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
               31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
               45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
               34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
               31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
               40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
               38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
               76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
               41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
               27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
               46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
               41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
               46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
               41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
               40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
               27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
               76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
               41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
               76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
               27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
               34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
               44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
               40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
               46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
               38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
               40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
               27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
               76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
               76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
               76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
               76, 76]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11]]      ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]      )      )
       self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]      )      )
       self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]      )      )




 @require_torch
 def      __magic_name__	(      self	:							Any      )	->						List[str]:
       import torch

       SCREAMING_SNAKE_CASE__     :				int									=JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer(**self.metas      )['''input_ids''']
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[
           torch.tensor([[
               0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
               31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
               31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
               40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
               79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
               77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
               27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
               37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
               32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
               77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
               77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
               46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
               77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
               77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
               77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
               77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
               64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
               40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
               40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
               38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
               31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
               41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
               46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
               41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
               31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
               31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
               44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
               31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
               38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
               40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
               27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
               31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
               34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
               31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
               31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
               45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
               31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
               15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
               11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
               45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
               41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
               44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
               46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
               27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
               35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
               77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
               31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
               41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
               77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
               40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
               77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
               27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
               77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
               77, 77, 77, 77, 77, 77]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]      ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]      )      )
       self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]      )      )
       self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]      )      )