


'''simple docstring'''
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def     a   (    __a				,      __a     )						->			float:



					'''simple docstring'''






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


def     a   (    __a				,      __a     )						->			list[list[list[float] | float]]:



					'''simple docstring'''






					if dataset.ndim != value_array.ndim:
										UpperCamelCase__     :int							      =				(
										    '''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__     :Dict							      =				(
															    '''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__     :List[Any]							      =				(
										    '''Input data have different datatype... '''
										    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
										)
										raise TypeError(__a     )

					UpperCamelCase__     :Union[str, Any]							      =				[]

					for value in value_array:
										UpperCamelCase__     :Optional[Any]							      =				euclidean(__a				,      dataset[0]     )
										UpperCamelCase__     :Tuple							      =				dataset[0].tolist()

										for dataset_value in dataset[1:]:
															UpperCamelCase__     :Tuple							      =				euclidean(__a				,      __a     )

															if dist > temp_dist:
																				UpperCamelCase__     :Union[str, Any]							      =				temp_dist
																				UpperCamelCase__     :Optional[Any]							      =				dataset_value.tolist()

										answer.append([vector, dist]     )

					return answer


def     a   (    __a				,      __a     )						->			float:



					'''simple docstring'''






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							   =  {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		__snake_case							   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)