"""simple docstring"""





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

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





def        a_  (   _lowerCAmelCase						:				Vector     ,				_lowerCAmelCase						:				Vector  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				Vector     ,				_lowerCAmelCase						:				Vector  ):





       '''simple docstring'''

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


if __name__ == "__main__":





       def        a_  (   ):





              '''simple docstring'''

              from timeit import timeit

              print('Without Numpy'  )
              print(
                  timeit(
                      'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'     ,				number=1_0000     ,				globals=globals()     ,				)  )
              print('With Numpy'  )
              print(
                  timeit(
                      'euclidean_distance([1, 2, 3], [4, 5, 6])'     ,				number=1_0000     ,				globals=globals()     ,				)  )






       benchmark()


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

