




from sklearn.neural_network import MLPClassifier

UpperCamelCase__						=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase__						=		[0, 1, 0, 0]


UpperCamelCase__						=		MLPClassifier(
    solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase__						=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase__						=		clf.predict(test)






def    snake_case   (				A     )				->				Dict:




	'''simple docstring'''


	return list(A     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__			:  Optional[Any]					     =     {
    """configuration_musicgen""": [
        """MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MusicgenConfig""",
        """MusicgenDecoderConfig""",
    ],
    """processing_musicgen""": ["""MusicgenProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Any					     =     [
      """MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MusicgenForConditionalGeneration""",
      """MusicgenForCausalLM""",
      """MusicgenModel""",
      """MusicgenPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_musicgen import (
      MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MusicgenConfig,
      MusicgenDecoderConfig,
  )
  from .processing_musicgen import MusicgenProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_musicgen import (
        MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        MusicgenForCausalLM,
        MusicgenForConditionalGeneration,
        MusicgenModel,
        MusicgenPreTrainedModel,
    )

else:
  import sys

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




