




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():
							import torch

							from transformers import CamembertModel


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

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							CamembertModel.from_pretrained('camembert-base'					)
						model.to(__lowercase					)

						lowerCamelCase_							:		int							   =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] ,       device=__lowercase ,       dtype=torch.long ,       )  # J'aime le camembert !
						with torch.no_grad():
							lowerCamelCase_							:		Any							   =							model(__lowercase					)['last_hidden_state']
						lowerCamelCase_							:		Dict							   =							torch.Size((1, 10, 768)					)
						self.assertEqual(output.shape ,       __lowercase					)
						# compare the actual values for a slice.
						lowerCamelCase_							:		Dict							   =							torch.tensor(
						    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]] ,       device=__lowercase ,       dtype=torch.float ,       )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3] ,       __lowercase ,       atol=1E-4					)					)







"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






