
"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right






@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''google/mt5-small''')

							_UpperCamelCase									=    tokenizer('''Hello there'''							,						return_tensors='''np''').input_ids
							_UpperCamelCase									=    tokenizer('''Hi I am'''							,						return_tensors='''np''').input_ids

							_UpperCamelCase									=    shift_tokens_right(__a							,						model.config.pad_token_id							,						model.config.decoder_start_token_id)

							_UpperCamelCase									=    model(__a							,						decoder_input_ids=__a).logits
							_UpperCamelCase									=    optax.softmax_cross_entropy(__a							,						onehot(__a							,						logits.shape[-1])).mean()

							_UpperCamelCase									=    -(labels.shape[-1] * loss.item())

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











from collections import Counter

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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).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]))
