


"""simple docstring"""







import os
import sys

import transformers


a__    :							Optional[int]            =					'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

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())
		print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
		print('''Torch version:''', None)

try:
		import deepspeed

		print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
		print('''DeepSpeed version:''', None)

try:
		import tensorflow as tf

		print('''TensorFlow version:''', tf.__version__)
		print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
		print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
		print('''TensorFlow version:''', None)




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

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

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

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

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

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

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
