


"""simple docstring"""







from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    Optional[NestedDataStructureLike[PathLike]] = None      ,      UpperCAmelCase__      :    Optional[NamedSplit] = None      ,      UpperCAmelCase__      :    Optional[Features] = None      ,      UpperCAmelCase__      :    str = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    Optional[int] = None      ,      **UpperCAmelCase__      :    Optional[int]      ,      )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    path_or_paths
						__SCREAMING_SNAKE_CASE						       =    split if split or isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       ) else "train"
						__SCREAMING_SNAKE_CASE						       =    features
						__SCREAMING_SNAKE_CASE						       =    cache_dir
						__SCREAMING_SNAKE_CASE						       =    keep_in_memory
						__SCREAMING_SNAKE_CASE						       =    streaming
						__SCREAMING_SNAKE_CASE						       =    num_proc
						__SCREAMING_SNAKE_CASE						       =    kwargs




	@abstractmethod
	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
						pass




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    Optional[Features] = None      ,      UpperCAmelCase__      :    str = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    Optional[int] = None      ,      **UpperCAmelCase__      :    int      ,      )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    features
						__SCREAMING_SNAKE_CASE						       =    cache_dir
						__SCREAMING_SNAKE_CASE						       =    keep_in_memory
						__SCREAMING_SNAKE_CASE						       =    streaming
						__SCREAMING_SNAKE_CASE						       =    num_proc
						__SCREAMING_SNAKE_CASE						       =    kwargs




	@abstractmethod
	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Union[Dataset, IterableDataset]:
						pass




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
