'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a   :	List[str] 							=   {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :	Union[str, Any] 							=   [
        "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CTRLForSequenceClassification",
        "CTRLLMHeadModel",
        "CTRLModel",
        "CTRLPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :	Optional[Any] 							=   [
        "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFCTRLForSequenceClassification",
        "TFCTRLLMHeadModel",
        "TFCTRLModel",
        "TFCTRLPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
    from .tokenization_ctrl import CTRLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ctrl import (
            CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
            CTRLForSequenceClassification,
            CTRLLMHeadModel,
            CTRLModel,
            CTRLPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ctrl import (
            TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCTRLForSequenceClassification,
            TFCTRLLMHeadModel,
            TFCTRLModel,
            TFCTRLPreTrainedModel,
        )

else:
    import sys

    _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''simple docstring'''




from collections import Counter

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

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


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






