

'''simple docstring'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase :      int					 =     [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase :      str					 =     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}







"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

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

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()


