


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import tensorflow as tf

			from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM

@require_tf
@require_sentencepiece
@require_tokenizers
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@slow
							def    __snake_case (							self	:  Any    )     ->   Any:
														__snake_case		:      Union[str, Any]										=       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    )
														__snake_case		:      Tuple										=       AutoTokenizer.from_pretrained("google/mt5-small"    )

														__snake_case		:      List[Any]										=       tokenizer("Hello there"   ,     return_tensors="tf"    ).input_ids
														__snake_case		:      Optional[Any]										=       tokenizer("Hi I am"   ,     return_tensors="tf"    ).input_ids

														__snake_case		:      Union[str, Any]										=       model(lowerCamelCase   ,     labels=lowerCamelCase    ).loss
														__snake_case		:      str										=       -tf.math.reduce_mean(lowerCamelCase    ).numpy()

														__snake_case		:      List[str]										=       -21.22_81_68
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 2E-4    )





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

