






def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      list     )    ->   Dict:





    """simple docstring"""

    _enforce_args(__lowercase      ,					__lowercase     )
    if n == 0:
        return 0
    __A       =						float("""-inf"""     )
    for i in range(1      ,					n + 1     ):
        __A       =						max(
            __lowercase      ,					prices[i - 1] + naive_cut_rod_recursive(n - i      ,					__lowercase     )     )

    return max_revue



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      list     )    ->   Dict:





    """simple docstring"""

    _enforce_args(__lowercase      ,					__lowercase     )
    __A       =						[float("""-inf"""     ) for _ in range(n + 1     )]
    return _top_down_cut_rod_recursive(__lowercase      ,					__lowercase      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      list      ,					__lowercase :      list     )    ->   Dict:





    """simple docstring"""

    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A       =						float("""-inf"""     )
        for i in range(1      ,					n + 1     ):
            __A       =						max(
                __lowercase      ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i      ,					__lowercase      ,					__lowercase     )      ,					)

        __A       =						max_revenue

    return max_rev[n]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      list     )    ->   Dict:





    """simple docstring"""

    _enforce_args(__lowercase      ,					__lowercase     )

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A       =						[float("""-inf"""     ) for _ in range(n + 1     )]
    __A       =						0

    for i in range(1      ,					n + 1     ):
        __A       =						max_rev[i]
        for j in range(1      ,					i + 1     ):
            __A       =						max(__lowercase      ,					prices[j - 1] + max_rev[i - j]     )

        __A       =						max_revenue_i

    return max_rev[n]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      list     )    ->   List[Any]:





    """simple docstring"""

    if n < 0:
        __A       =						f"n must be greater than or equal to 0. Got n = {n}"
        raise ValueError(__lowercase     )

    if n > len(__lowercase     ):
        __A       =						(
            """Each integral piece of rod must have a corresponding price. """
            f"Got n = {n} but length of prices = {len(__lowercase     )}"
        )
        raise ValueError(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Any:





    """simple docstring"""

    __A       =						[6, 1_0, 1_2, 1_5, 2_0, 2_3]
    __A       =						len(__lowercase     )

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A       =						3_6

    __A       =						top_down_cut_rod(__lowercase      ,					__lowercase     )
    __A       =						bottom_up_cut_rod(__lowercase      ,					__lowercase     )
    __A       =						naive_cut_rod_recursive(__lowercase      ,					__lowercase     )

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a   :							Optional[int]						= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Tuple						= ["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Any						= [
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

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




