






'''simple docstring'''




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

import yaml







class       __lowercase					(yaml.SafeLoader    ):

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





	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Any=False):
			UpperCamelCase__				:					Union[str, Any]					=					super().construct_mapping(UpperCAmelCase_  ,					deep=UpperCAmelCase_)
			self._check_no_duplicates_on_constructed_node(UpperCAmelCase_)
			return mapping







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple[Optional[str], str]:
		UpperCamelCase__				:					str					=					list(readme_content.splitlines())
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				UpperCamelCase__				:					Dict					=					full_content[1:].index('---') + 1
				UpperCamelCase__				:					Any					=					'\n'.join(full_content[1:sep_idx])
				return yamlblock, "\n".join(full_content[sep_idx + 1 :])

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







class       __lowercase					(__lowerCamelCase    ):
	# class attributes
	_lowerCamelCase              =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata

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

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

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

	@classmethod
	def      __UpperCamelCase							(      cls   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Any					=					yaml.load(UpperCAmelCase_  ,					Loader=_NoDuplicateSafeLoader) or {}

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





	def      __UpperCamelCase							(      self   :						Any):
			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')


lowerCAmelCase__          =						{
    '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

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

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












'''simple docstring'''




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					self.__linear
			elif kernel == "rbf":
					if self.gamma == 0:
							raise ValueError('rbf kernel requires gamma')
					if not isinstance(self.gamma  ,					(float, int)):
							raise ValueError('gamma must be float or int')
					if not self.gamma > 0:
							raise ValueError('gamma must be > 0')
					UpperCamelCase__				:					Union[str, Any]					=					self.__rbf
					# in the future, there could be a default value like in sklearn
					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
					# previously it was 1/(n_features)
			else:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					classes

			# using Wolfe's Dual to calculate w.
			# Primal problem: minimize 1/2*norm_squared(w)
			#   constraint: yn(w . xn + b) >= 1
			#
			# With l a vector
			# Dual problem: maximize sum_n(ln) -
			#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
			#   constraint: self.C >= ln >= 0
			#           and sum_n(ln*yn) = 0
			# Then we get w using w = sum_n(ln*yn*xn)
			# At the end we can get b ~= mean(yn - w . xn)
			#
			# Since we use kernels, we only need l_star to calculate b
			# and to classify observations

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

			# calculating mean offset of separation plane to points
			UpperCamelCase__				:					Any					=					0
			for i in range(UpperCAmelCase_):
					for j in range(UpperCAmelCase_):
							s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
							    observations[i]  ,					observations[j])
			UpperCamelCase__				:					List[str]					=					s / n





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




