





"""simple docstring"""



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def 		__lowercase					(       snake_case_  :  np.ndarray    ,snake_case_  :  np.ndarray						)       ->float:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  np.ndarray    ,snake_case_  :  np.ndarray						)       ->list[list[list[float] | float]]:



    '''simple docstring'''







    if dataset.ndim != value_array.ndim:
        __A   :						Optional[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]:
            __A   :						List[str]									=					(
                '''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:
        __A   :						Union[str, Any]									=					(
            '''Input data have different datatype... '''
            F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
        )
        raise TypeError(snake_case_						)

    __A   :						Optional[int]									=					[]

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

        for dataset_value in dataset[1:]:
            __A   :						Tuple									=					euclidean(snake_case_    ,snake_case_						)

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

        answer.append([vector, dist]						)

    return answer







def 		__lowercase					(       snake_case_  :  np.ndarray    ,snake_case_  :  np.ndarray						)       ->float:



    '''simple docstring'''







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


if __name__ == "__main__":
       import doctest

       doctest.testmod()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




