





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def    A_(			A				,      A   ):
	return math.sqrt(sum(pow(a - b				,      2   ) for a, b in zip(A				,      A   )   )   )




def    A_(			A				,      A   ):

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

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

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

	UpperCAmelCase_								=     []

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

		for dataset_value in dataset[1:]:
			UpperCAmelCase_								=     euclidean(A				,      A   )

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

		answer.append([vector, dist]   )

	return answer




def    A_(			A				,      A   ):
	return np.dot(A				,      A   ) / (norm(A   ) * norm(A   ))


if __name__ == "__main__":
				import doctest

				doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__   : List[Any]					  = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : List[str]					  = [
				    """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SEWForCTC""",
				    """SEWForSequenceClassification""",
				    """SEWModel""",
				    """SEWPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_sew import (
								    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SEWForCTC,
								    SEWForSequenceClassification,
								    SEWModel,
								    SEWPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__   : str					  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


