






'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     _enforce_args(_UpperCamelCase,						_UpperCamelCase     )
     if n == 0:
          return 0
     __lowerCamelCase						:       Tuple				   =				float("""-inf"""     )
     for i in range(1,						n + 1     ):
          __lowerCamelCase						:       int				   =				max(
              _UpperCamelCase,						prices[i - 1] + naive_cut_rod_recursive(n - i,						_UpperCamelCase     )     )

     return max_revue
def     __lowercase  (_lowercase,						_lowercase     )   ->     Optional[int]:


     """simple docstring"""
     _enforce_args(_UpperCamelCase,						_UpperCamelCase     )
     __lowerCamelCase						:       Dict				   =				[float("""-inf"""     ) for _ in range(n + 1     )]
     return _top_down_cut_rod_recursive(_UpperCamelCase,						_UpperCamelCase,						_UpperCamelCase     )
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""


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

          __lowerCamelCase						:       List[Any]				   =				max_revenue

     return max_rev[n]
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     _enforce_args(_UpperCamelCase,						_UpperCamelCase     )

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

     for i in range(1,						n + 1     ):
          __lowerCamelCase						:       Union[str, Any]				   =				max_rev[i]
          for j in range(1,						i + 1     ):
               __lowerCamelCase						:       List[Any]				   =				max(_UpperCamelCase,						prices[j - 1] + max_rev[i - j]     )

          __lowerCamelCase						:       str				   =				max_revenue_i

     return max_rev[n]
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""


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

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


     """simple docstring"""
     __lowerCamelCase						:       int				   =				[6, 10, 12, 15, 20, 23]
     __lowerCamelCase						:       Optional[Any]				   =				len(_UpperCamelCase     )

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

     __lowerCamelCase						:       str				   =				top_down_cut_rod(_UpperCamelCase,						_UpperCamelCase     )
     __lowerCamelCase						:       List[str]				   =				bottom_up_cut_rod(_UpperCamelCase,						_UpperCamelCase     )
     __lowerCamelCase						:       Tuple				   =				naive_cut_rod_recursive(_UpperCamelCase,						_UpperCamelCase     )

     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()











'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]     )
               else:
                    wrong_args.append(_lowercase     )
          if len(_lowercase     ) > 0:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



