


"""simple docstring"""





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


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


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):

      if dataset.ndim != value_array.ndim:
            lowerCAmelCase    :		List[Any]				=      (
                '''Wrong input data\'s dimensions... '''
                f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
            )
            raise ValueError(_snake_case	)

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

      if dataset.dtype != value_array.dtype:
            lowerCAmelCase    :		Optional[Any]				=      (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(_snake_case	)

      lowerCAmelCase    :		str				=      []

      for value in value_array:
            lowerCAmelCase    :		int				=      euclidean(_snake_case       ,      dataset[0]	)
            lowerCAmelCase    :		Union[str, Any]				=      dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  lowerCAmelCase    :		Any				=      euclidean(_snake_case       ,      _snake_case	)

                  if dist > temp_dist:
                        lowerCAmelCase    :		List[Any]				=      temp_dist
                        lowerCAmelCase    :		Tuple				=      dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer


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


if __name__ == "__main__":
 import doctest

 doctest.testmod()



from typing import TYPE_CHECKING

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


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





