
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def 	A_ (							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 	A_ (							snake_case							:   np.ndarray ,      snake_case							:   np.ndarray )      ->    list[list[list[float] | float]]:



						'''simple docstring'''

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

						__UpperCamelCase       =			[]

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

												for dataset_value in dataset[1:]:
																		__UpperCamelCase       =			euclidean(snake_case ,      snake_case )

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

												answer.append([vector, dist] )

						return answer




def 	A_ (							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()






# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


lowercase__							:							List[str]									=			"3"

print("Python version:", sys.version)

print("OS platform:", platform.platform())
print("OS architecture:", platform.machine())

try:
				import torch

				print("Torch version:", torch.__version__)
				print("Cuda available:", torch.cuda.is_available())
				print("Cuda version:", torch.version.cuda)
				print("CuDNN version:", torch.backends.cudnn.version())
				print("Number of GPUs available:", torch.cuda.device_count())
except ImportError:
				print("Torch version:", None)

try:
				import transformers

				print("transformers version:", transformers.__version__)
except ImportError:
				print("transformers version:", None)




