



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__A   :							Tuple										=   TypeVar("T")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				return (position - 1) // 2







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				return (2 * position) + 1







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				return (2 * position) + 2
class 			_a (					Generic[T]):
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]   )->None:
							_UpperCAmelCase					=    []
							_UpperCAmelCase					=    {}
							_UpperCAmelCase					=    0



			def __len__(		self     :    Dict   )->int:
							return self.elements



			def __repr__(		self     :    Dict   )->str:
							return str(self.heap   )



			def  lowercase__						(		self     :    Tuple   )->bool:
							return self.elements == 0



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    T				,   __UpperCamelCase     :    int   )->None:
							self.heap.append((elem, weight)   )
							_UpperCAmelCase					=    self.elements
							self.elements += 1
							self._bubble_up(UpperCAmelCase_   )



			def  lowercase__						(		self     :    Optional[Any]   )->T:



							if self.elements > 1:
											self._swap_nodes(0				,   self.elements - 1   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[0]
											self._bubble_down(UpperCAmelCase_   )
							return elem



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    T				,   __UpperCamelCase     :    int   )->None:
							_UpperCAmelCase					=    self.position_map[elem]
							_UpperCAmelCase					=    (elem, weight)
							if position > 0:
											_UpperCAmelCase					=    get_parent_position(UpperCAmelCase_   )
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[parent_position]
											if parent_weight > weight:
															self._bubble_up(UpperCAmelCase_   )
											else:
															self._bubble_down(UpperCAmelCase_   )
							else:
											self._bubble_down(UpperCAmelCase_   )



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    T   )->None:
							_UpperCAmelCase					=    self.position_map[elem]
							if curr_pos == 0:
											return None
							_UpperCAmelCase					=    get_parent_position(UpperCAmelCase_   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[curr_pos]
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[parent_position]
							if parent_weight > weight:
											self._swap_nodes(UpperCAmelCase_				,   UpperCAmelCase_   )
											return self._bubble_up(UpperCAmelCase_   )
							return None



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    T   )->None:
							_UpperCAmelCase					=    self.position_map[elem]
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[curr_pos]
							_UpperCAmelCase					=    get_child_left_position(UpperCAmelCase_   )
							_UpperCAmelCase					=    get_child_right_position(UpperCAmelCase_   )
							if child_left_position < self.elements and child_right_position < self.elements:
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[child_left_position]
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[child_right_position]
											if child_right_weight < child_left_weight and child_right_weight < weight:
															self._swap_nodes(UpperCAmelCase_				,   UpperCAmelCase_   )
															return self._bubble_down(UpperCAmelCase_   )
							if child_left_position < self.elements:
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[child_left_position]
											if child_left_weight < weight:
															self._swap_nodes(UpperCAmelCase_				,   UpperCAmelCase_   )
															return self._bubble_down(UpperCAmelCase_   )
							else:
											return None
							if child_right_position < self.elements:
											_UpperCAmelCase		,						_UpperCAmelCase					=    self.heap[child_right_position]
											if child_right_weight < weight:
															self._swap_nodes(UpperCAmelCase_				,   UpperCAmelCase_   )
															return self._bubble_down(UpperCAmelCase_   )
							return None



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->None:
							_UpperCAmelCase					=    self.heap[nodea_pos][0]
							_UpperCAmelCase					=    self.heap[nodea_pos][0]
							_UpperCAmelCase		,						_UpperCAmelCase					=    (
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							_UpperCAmelCase					=    nodea_pos
							_UpperCAmelCase					=    nodea_pos



class 			_a (					Generic[T]):
			"""simple docstring"""



			def __init__(		self     :    Any   )->None:
							_UpperCAmelCase					=    {}
							_UpperCAmelCase					=    0



			def __repr__(		self     :    Tuple   )->str:
							return str(self.connections   )



			def __len__(		self     :    Dict   )->int:
							return self.nodes



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    T   )->None:



							if node not in self.connections:
											_UpperCAmelCase					=    {}
											self.nodes += 1



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    T				,   __UpperCamelCase     :    T				,   __UpperCamelCase     :    int   )->None:
							self.add_node(UpperCAmelCase_   )
							self.add_node(UpperCAmelCase_   )
							_UpperCAmelCase					=    weight
							_UpperCAmelCase					=    weight







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    {node: maxsize for node in graph.connections}
				_UpperCAmelCase					=    {node: None for node in graph.connections}

				_UpperCAmelCase					=    MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				_UpperCAmelCase					=    priority_queue.extract_min()
				_UpperCAmelCase					=    0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												_UpperCAmelCase					=    dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(_SCREAMING_SNAKE_CASE					,     dist[neighbour]				)
												_UpperCAmelCase					=    node

    # running prim's algorithm
				while not priority_queue.is_empty():
								_UpperCAmelCase					=    priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																_UpperCAmelCase					=    dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(_SCREAMING_SNAKE_CASE					,     dist[neighbour]				)
																_UpperCAmelCase					=    node
				return dist, parent








"""simple docstring"""


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

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



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



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    list(readme_content.splitlines()				)
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								_UpperCAmelCase					=    full_content[1:].index('''---'''				) + 1
								_UpperCAmelCase					=    '''\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"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(readme_file.read()   )
							if yaml_string is not None:
											return cls.from_yaml_string(__UpperCamelCase   )
							else:
											return cls()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							if path.exists():
											with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
															_UpperCAmelCase					=    readme_file.read()
							else:
											_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self._to_readme(__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as readme_file:
											readme_file.write(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							if readme_content is not None:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(__UpperCamelCase   )
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
							else:
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
							return full_content



			@classmethod
			def  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_UpperCAmelCase					=    yaml.load(__UpperCamelCase				,   Loader=_NoDuplicateSafeLoader   ) or {}

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



			def  lowercase__						(		self     :    str   )->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=__UpperCamelCase				,   allow_unicode=__UpperCamelCase				,   encoding='''utf-8'''				,   ).decode('''utf-8'''   )



__A   :							str										=   {
    "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   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



