
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase      :							Optional[int]          =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							Union[str, Any]          =					["SplinterTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							Any          =					[
          "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SplinterForQuestionAnswering",
          "SplinterForPreTraining",
          "SplinterLayer",
          "SplinterModel",
          "SplinterPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
      from .tokenization_splinter import SplinterTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_splinter_fast import SplinterTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_splinter import (
                SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
                SplinterForPreTraining,
                SplinterForQuestionAnswering,
                SplinterLayer,
                SplinterModel,
                SplinterPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase      :							Tuple          =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



