
"""simple docstring"""

from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

SCREAMING_SNAKE_CASE_    					=						typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
SCREAMING_SNAKE_CASE_    					=						typing.Union[np.floataa, int, float]  # noqa: UP007




def     lowercase__						(					lowerCAmelCase				:					Vector ,				lowerCAmelCase				:					Vector ) ->      VectorOut:






    """simple docstring"""




    return np.sqrt(np.sum((np.asarray(lowerCAmelCase ) - np.asarray(lowerCAmelCase )) ** 2 ) )




def     lowercase__						(					lowerCAmelCase				:					Vector ,				lowerCAmelCase				:					Vector ) ->      VectorOut:






    """simple docstring"""




    return sum((va - va) ** 2 for va, va in zip(lowerCAmelCase ,				lowerCAmelCase ) ) ** (1 / 2)


if __name__ == "__main__":




   def     lowercase__						(					) ->      None:






       """simple docstring"""




       from timeit import timeit

       print('Without Numpy' )
       print(
           timeit(
               'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' ,				number=10_000 ,				globals=globals() ,				) )
       print('With Numpy' )
       print(
           timeit(
               'euclidean_distance([1, 2, 3], [4, 5, 6])' ,				number=10_000 ,				globals=globals() ,				) )







   benchmark()





"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"encoder-decoder"
      __SCREAMING_SNAKE_CASE   :							Any							      =	True




      def __init__(							self   ,       **lowercase_							)						->					str:
          super().__init__(**lowercase_							)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          UpperCAmelCase									=					kwargs.pop('encoder'							)
          UpperCAmelCase									=					encoder_config.pop('model_type'							)
          UpperCAmelCase									=					kwargs.pop('decoder'							)
          UpperCAmelCase									=					decoder_config.pop('model_type'							)

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					True




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_   ,       **lowercase_							)						->					PretrainedConfig:
          logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
          UpperCAmelCase									=					True
          UpperCAmelCase									=					True

          return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.encoder.to_dict()
          UpperCAmelCase									=					self.decoder.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output



