



'''simple docstring'''





from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class __A			(  yaml.SafeLoader						):



 '''simple docstring'''







 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      _a      =  [self.constructed_objects[key_node] for key_node, _ in node.value]
      _a      =  [tuple(A       ) if isinstance(A ,   A       ) else key for key in keys]
      _a      =  Counter(A       )
      _a      =  [key for key in counter if counter[key] > 1]
      if duplicate_keys:
           raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''       )



 def 					a__  (self ,   A ,   A=False       )    ->     Any:


      """simple docstring"""



      _a      =  super().construct_mapping(A ,   deep=A       )
      self._check_no_duplicates_on_constructed_node(A       )
      return mapping





def lowerCAmelCase  (__A):



     """simple docstring"""



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

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






class __A			(  __lowerCamelCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       {'train_eval_index'}  # train-eval-index in the YAML metadata



 @classmethod
 def 					a__  (cls ,   A       )    ->     Any:


      """simple docstring"""



      with open(A ,   encoding='''utf-8'''       ) as readme_file:
           _a      =  _split_yaml_from_readme(readme_file.read()       )
      if yaml_string is not None:
           return cls.from_yaml_string(A       )
      else:
           return cls()



 def 					a__  (self ,   A       )    ->     Tuple:


      """simple docstring"""



      if path.exists():
           with open(A ,   encoding='''utf-8'''       ) as readme_file:
                _a      =  readme_file.read()
      else:
           _a      =  None
      _a      =  self._to_readme(A       )
      with open(A ,   '''w''' ,   encoding='''utf-8'''       ) as readme_file:
           readme_file.write(A       )



 def 					a__  (self ,   A = None       )    ->     List[str]:


      """simple docstring"""



      if readme_content is not None:
           _a      =  _split_yaml_from_readme(A       )
           _a      =  """---\n""" + self.to_yaml_string() + """---\n""" + content
      else:
           _a      =  """---\n""" + self.to_yaml_string() + """---\n"""
      return full_content



 @classmethod
 def 					a__  (cls ,   A       )    ->     str:


      """simple docstring"""



      _a      =  yaml.load(A ,   Loader=_NoDuplicateSafeLoader       ) or {}

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



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



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





lowercase_		       =  {
    "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

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

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










from __future__ import annotations

import typing
from collections import Counter







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    typing.Counter[int]:
  __snake_case:						typing.Counter[int]   =						Counter()
  for base in range(1					,					max_perimeter + 1):
    for perpendicular in range(SCREAMING_SNAKE_CASE__					,					max_perimeter + 1):
      __snake_case:						Dict   =						(base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(SCREAMING_SNAKE_CASE__):
        __snake_case:						Any   =						int(base + perpendicular + hypotenuse)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1000)   ->    int:
  __snake_case:						List[str]   =						pythagorean_triple(SCREAMING_SNAKE_CASE__)
  return triplets.most_common(1)[0][0]


if __name__ == "__main__":
      print(f'Perimeter {solution()} has maximum solutions')






