




"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def     a__ (				snake_case__       , snake_case__				)						->					float:
			return math.sqrt(sum(pow(a - b       , 2				) for a, b in zip(snake_case__       , snake_case__				)				)				)



def     a__ (				snake_case__       , snake_case__				)						->					list[list[list[float] | float]]:

			if dataset.ndim != value_array.ndim:
						lowerCamelCase			      =					(
						    """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]:
									lowerCamelCase			      =					(
									    """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:
						lowerCamelCase			      =					(
						    """Input data have different datatype... """
						    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
						)
						raise TypeError(snake_case__				)

			lowerCamelCase			      =					[]

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

						for dataset_value in dataset[1:]:
									lowerCamelCase			      =					euclidean(snake_case__       , snake_case__				)

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

						answer.append([vector, dist]				)

			return answer



def     a__ (				snake_case__       , snake_case__				)						->					float:
			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__				)				)



