

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:

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

		UpperCAmelCase_						=				[]

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

				for dataset_value in dataset[1:]:
						UpperCAmelCase_						=				euclidean(__lowerCAmelCase			,     __lowerCAmelCase )

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

				answer.append([vector, dist] )

		return answer







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							str:
		return np.dot(__lowerCAmelCase			,     __lowerCAmelCase ) / (norm(__lowerCAmelCase ) * norm(__lowerCAmelCase ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

