


def    lowercase_	(	_A    :					int					,      _A    :					list		):

 """simple docstring"""




 _enforce_args(_A					,      _A		)
 if n == 0:
  return 0
 lowerCamelCase__	:       Any					=						float("-inf"		)
 for i in range(1					,      n + 1		):
  lowerCamelCase__	:       List[str]					=						max(
      _A					,      prices[i - 1] + naive_cut_rod_recursive(n - i					,      _A		)		)

 return max_revue

def    lowercase_	(	_A    :					int					,      _A    :					list		):

 """simple docstring"""




 _enforce_args(_A					,      _A		)
 lowerCamelCase__	:       int					=						[float("-inf"		) for _ in range(n + 1		)]
 return _top_down_cut_rod_recursive(_A					,      _A					,      _A		)

def    lowercase_	(	_A    :					int					,      _A    :					list					,      _A    :					list		):

 """simple docstring"""




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

  lowerCamelCase__	:       List[Any]					=						max_revenue

 return max_rev[n]

def    lowercase_	(	_A    :					int					,      _A    :					list		):

 """simple docstring"""




 _enforce_args(_A					,      _A		)

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

 for i in range(1					,      n + 1		):
  lowerCamelCase__	:       Tuple					=						max_rev[i]
  for j in range(1					,      i + 1		):
   lowerCamelCase__	:       List[str]					=						max(_A					,      prices[j - 1] + max_rev[i - j]		)

  lowerCamelCase__	:       Any					=						max_revenue_i

 return max_rev[n]

def    lowercase_	(	_A    :					int					,      _A    :					list		):

 """simple docstring"""




 if n < 0:
  lowerCamelCase__	:       str					=						F"n must be greater than or equal to 0. Got n = {n}"
  raise ValueError(_A		)

 if n > len(_A		):
  lowerCamelCase__	:       List[Any]					=						(
      "Each integral piece of rod must have a corresponding price. "
      F"Got n = {n} but length of prices = {len(_A		)}"
  )
  raise ValueError(_A		)

def    lowercase_	(	):

 """simple docstring"""




 lowerCamelCase__	:       Optional[Any]					=						[6, 10, 12, 15, 20, 23]
 lowerCamelCase__	:       Tuple					=						len(_A		)

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 lowerCamelCase__	:       Any					=						36

 lowerCamelCase__	:       Optional[Any]					=						top_down_cut_rod(_A					,      _A		)
 lowerCamelCase__	:       int					=						bottom_up_cut_rod(_A					,      _A		)
 lowerCamelCase__	:       Union[str, Any]					=						naive_cut_rod_recursive(_A					,      _A		)

 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_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


