

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      float:





    '''simple docstring'''






    return math.sqrt(sum(pow(a - b    ,			2      ) for a, b in zip(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )      )      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      list[list[list[float] | float]]:





    '''simple docstring'''






    if dataset.ndim != value_array.ndim:
        A__          =   (
            'Wrong input data\'s dimensions... '
            f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )

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

    if dataset.dtype != value_array.dtype:
        A__          =   (
            'Input data have different datatype... '
            f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
        )
        raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__          =   []

    for value in value_array:
        A__          =   euclidean(SCREAMING_SNAKE_CASE__    ,			dataset[0]      )
        A__          =   dataset[0].tolist()

        for dataset_value in dataset[1:]:
            A__          =   euclidean(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

            if dist > temp_dist:
                A__          =   temp_dist
                A__          =   dataset_value.tolist()

        answer.append([vector, dist]      )

    return answer
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      float:





    '''simple docstring'''






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


if __name__ == "__main__":
     import doctest

     doctest.testmod()




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

