





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_     :      Any								={
    """configuration_unispeech_sat""": ["""UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechSatConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_     :      Union[str, Any]								=[
          """UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """UniSpeechSatForAudioFrameClassification""",
          """UniSpeechSatForCTC""",
          """UniSpeechSatForPreTraining""",
          """UniSpeechSatForSequenceClassification""",
          """UniSpeechSatForXVector""",
          """UniSpeechSatModel""",
          """UniSpeechSatPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_unispeech_sat import (
                UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                UniSpeechSatForAudioFrameClassification,
                UniSpeechSatForCTC,
                UniSpeechSatForPreTraining,
                UniSpeechSatForSequenceClassification,
                UniSpeechSatForXVector,
                UniSpeechSatModel,
                UniSpeechSatPreTrainedModel,
            )

else:
      import sys

      A_     :      Dict								=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













"""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]}'
)






