





from sklearn.neural_network import MLPClassifier

__A    :   Union[str, Any]          =       [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A    :   int          =       [0, 1, 0, 0]


__A    :   Any          =       MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A    :   Dict          =       [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A    :   List[str]          =       clf.predict(test)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			return list(UpperCamelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__A    :   Any          =       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Union[str, Any]          =       ["CpmTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[Any]          =       ["CpmTokenizerFast"]


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm import CpmTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm_fast import CpmTokenizerFast

else:
				import sys

				__A    :   str          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



