

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      float:





    '''simple docstring'''






    return math.sqrt(sum(pow(a - b    ,			2      ) for a, b in zip(snake_case__    ,			snake_case__      )      )      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      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(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(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(snake_case__      )

    A__          =   []

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

        for dataset_value in dataset[1:]:
            A__          =   euclidean(snake_case__    ,			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__			:	int    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      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 math import ceil



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					list(range(0       , snake_case__				)				)

			lowerCamelCase			      =					[item for sublist in list(device_map.values()				) for item in sublist]

			# Duplicate check
			lowerCamelCase			      =					[]
			for i in device_map_blocks:
						if device_map_blocks.count(snake_case__				) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(snake_case__				)
    # Missing blocks
			lowerCamelCase			      =					[i for i in blocks if i not in device_map_blocks]
			lowerCamelCase			      =					[i for i in device_map_blocks if i not in blocks]

			if len(snake_case__				) != 0:
						raise ValueError(
						    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
						    """ These attention blocks were specified more than once: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
						    """blocks to a device on the device_map: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
						    + str(snake_case__				)				)



def     a__ (				snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					list(range(snake_case__				)				)
			lowerCamelCase			      =					int(ceil(n_layers / len(snake_case__				)				)				)
			lowerCamelCase			      =					[layers[i : i + n_blocks] for i in range(0       , snake_case__       , snake_case__				)]

			return dict(zip(snake_case__       , snake_case__				)				)



