
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def        a_		(       __lowercase   :	np.ndarray       ,					__lowercase   :	np.ndarray ) ->  float:
			return math.sqrt(sum(pow(a - b       ,					2 ) for a, b in zip(__lowercase       ,					__lowercase ) ) )






def        a_		(       __lowercase   :	np.ndarray       ,					__lowercase   :	np.ndarray ) ->  list[list[list[float] | float]]:

			if dataset.ndim != value_array.ndim:
						_snake_case		=			(
						    'Wrong input data\'s dimensions... '
						    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__lowercase )

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

			if dataset.dtype != value_array.dtype:
						_snake_case		=			(
						    'Input data have different datatype... '
						    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__lowercase )

			_snake_case		=			[]

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

						for dataset_value in dataset[1:]:
									_snake_case		=			euclidean(__lowercase       ,					__lowercase )

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

						answer.append([vector, dist] )

			return answer






def        a_		(       __lowercase   :	np.ndarray       ,					__lowercase   :	np.ndarray ) ->  float:
			return np.dot(__lowercase       ,					__lowercase ) / (norm(__lowercase ) * norm(__lowercase ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch




@require_torch
@require_optimum
@slow
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

				self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			model.reverse_bettertransformer()

				self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

							self.assertFalse(
							    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase	):
										model.save_pretrained(lowercase	)

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)