

'''simple docstring'''

from typing import TYPE_CHECKING

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


a       :      List[Any]  	=			{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a       :      Optional[Any]  	=			["FlavaFeatureExtractor"]
    a       :      List[Any]  	=			["FlavaImageProcessor"]
    a       :      List[str]  	=			["FlavaProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a       :      List[Any]  	=			[
        "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "FlavaForPreTraining",
        "FlavaImageCodebook",
        "FlavaImageModel",
        "FlavaModel",
        "FlavaMultimodalModel",
        "FlavaPreTrainedModel",
        "FlavaTextModel",
    ]

if TYPE_CHECKING:
    from .configuration_flava import (
        FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
        FlavaConfig,
        FlavaImageCodebookConfig,
        FlavaImageConfig,
        FlavaMultimodalConfig,
        FlavaTextConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_flava import FlavaFeatureExtractor
        from .image_processing_flava import FlavaImageProcessor
        from .processing_flava import FlavaProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flava import (
            FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlavaForPreTraining,
            FlavaImageCodebook,
            FlavaImageModel,
            FlavaModel,
            FlavaMultimodalModel,
            FlavaPreTrainedModel,
            FlavaTextModel,
        )

else:
    import sys

    a       :      int  	=			_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[Any]  	=			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       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

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