


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase       :      Union[str, Any]	=	{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Union[str, Any]	=	["DeiTFeatureExtractor"]
				_lowercase       :      Dict	=	["DeiTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Optional[int]	=	[
				    "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DeiTForImageClassification",
				    "DeiTForImageClassificationWithTeacher",
				    "DeiTForMaskedImageModeling",
				    "DeiTModel",
				    "DeiTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Optional[Any]	=	[
				    "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFDeiTForImageClassification",
				    "TFDeiTForImageClassificationWithTeacher",
				    "TFDeiTForMaskedImageModeling",
				    "TFDeiTModel",
				    "TFDeiTPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_deit import DeiTFeatureExtractor
								from .image_processing_deit import DeiTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deit import (
								    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DeiTForImageClassification,
								    DeiTForImageClassificationWithTeacher,
								    DeiTForMaskedImageModeling,
								    DeiTModel,
								    DeiTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deit import (
								    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDeiTForImageClassification,
								    TFDeiTForImageClassificationWithTeacher,
								    TFDeiTForMaskedImageModeling,
								    TFDeiTModel,
								    TFDeiTPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Tuple	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







from collections import Counter

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

_lowercase       :      Any	=	datasets.load_iris()

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







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



