





"""simple docstring"""


import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax







@require_flax
class 	__a   :



     def 						snake_case_			(       self   ):
            _lowerCamelCase	,	_lowerCamelCase    	=					self.prepare_init_args_and_inputs_for_common()

            _lowerCamelCase    	=					self.model_class(**a__   )
            _lowerCamelCase    	=					model.init(inputs_dict['prng_key']					,     inputs_dict['sample']   )
            jax.lax.stop_gradient(a__   )

            _lowerCamelCase    	=					model.apply(a__					,     inputs_dict['sample']   )

            if isinstance(a__					,     a__   ):
                   _lowerCamelCase    	=					output.sample

            self.assertIsNotNone(a__   )
            _lowerCamelCase    	=					inputs_dict['sample'].shape
            self.assertEqual(output.shape					,     a__					,     'Input and output shapes do not match'   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase	,	_lowerCamelCase    	=					self.prepare_init_args_and_inputs_for_common()

            _lowerCamelCase    	=					16
            _lowerCamelCase    	=					(16, 32)

            _lowerCamelCase    	=					self.model_class(**a__   )
            _lowerCamelCase    	=					model.init(inputs_dict['prng_key']					,     inputs_dict['sample']   )
            jax.lax.stop_gradient(a__   )

            _lowerCamelCase    	=					model.apply(a__					,     inputs_dict['sample']   )

            if isinstance(a__					,     a__   ):
                   _lowerCamelCase    	=					output.sample

            self.assertIsNotNone(a__   )
            _lowerCamelCase    	=					inputs_dict['sample'].shape
            self.assertEqual(output.shape					,     a__					,     'Input and output shapes do not match'   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					'kwargs' in inspect.signature(self.model_class.__init__   ).parameters
            _lowerCamelCase    	=					len(self.model_class._deprecated_kwargs   ) > 0

            if has_kwarg_in_model_class and not has_deprecated_kwarg:
                   raise ValueError(
                       F'{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'
                       ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
                       ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
                       ' [<deprecated_argument>]`'   )

            if not has_kwarg_in_model_class and has_deprecated_kwarg:
                   raise ValueError(
                       F'{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'
                       ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
                       F' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'
                       ' from `_deprecated_kwargs = [<deprecated_argument>]`'   )













"""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]}'
)






