






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCamelCase   :					List[str]   							=					{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[int]   							=					[
		    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieForCausalLM''',
		    '''ErnieForMaskedLM''',
		    '''ErnieForMultipleChoice''',
		    '''ErnieForNextSentencePrediction''',
		    '''ErnieForPreTraining''',
		    '''ErnieForQuestionAnswering''',
		    '''ErnieForSequenceClassification''',
		    '''ErnieForTokenClassification''',
		    '''ErnieModel''',
		    '''ErniePreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie import (
				    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieForCausalLM,
				    ErnieForMaskedLM,
				    ErnieForMultipleChoice,
				    ErnieForNextSentencePrediction,
				    ErnieForPreTraining,
				    ErnieForQuestionAnswering,
				    ErnieForSequenceClassification,
				    ErnieForTokenClassification,
				    ErnieModel,
				    ErniePreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase   :					List[str]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

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

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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


if __name__ == "__main__":
		import doctest

		doctest.testmod()




