





"""simple docstring"""





from collections import Counter

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

__A											=						datasets.load_iris()

__A											=						np.array(data["""data"""])
__A											=						np.array(data["""target"""])
__A											=						data["""target_names"""]

__A							,				__A							,				__A							,				__A											=						train_test_split(X, y)
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Dict:





  """simple docstring"""


  return np.linalg.norm(np.array(_SCREAMING_SNAKE_CASE     ) - np.array(_SCREAMING_SNAKE_CASE     )     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=5     )   ->Optional[Any]:





  """simple docstring"""


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

'''simple docstring'''


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				str						)				->						Any:
								__UpperCAmelCase      					=      SMALL_MODEL_IDENTIFIER
								__UpperCAmelCase      					=      '''pt'''
								__UpperCAmelCase      					=      '''tf'''





							def    snake_case__    (   self					:				int  ,		__a					:				int						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      AutoModel.from_pretrained(self.test_model						)
								model_pt.save_pretrained(__a						)





							def    snake_case__    (   self					:				Any  ,		__a					:				Optional[int]						)				->						Tuple:
								__UpperCAmelCase      					=      TFAutoModel.from_pretrained(self.test_model  ,		from_pt=__a						)
								model_tf.save_pretrained(__a						)





							def    snake_case__    (   self					:				Dict						)				->						Dict:
								__UpperCAmelCase      					=      '''mock_framework'''

								# Framework provided - return whatever the user provides
								__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model  ,		__a						)
								self.assertEqual(__a  ,		__a						)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)





							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)






							def    snake_case__    (   self					:				str						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# PyTorch not in environment -> use TensorFlow
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Both in environment -> use PyTorch
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# Both not in environment -> raise error
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
