




"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







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







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    '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]:
												_lowerCamelCase     :			Optional[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:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)