






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
	import torch

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


snake_case__      :	str       	=      logging.get_logger(__name__)


@dataclass
class 						_a       (  UpperCAmelCase__    ):






		"""simple docstring"""





		A_						=   [
		    """no_inference""",
		    """no_cuda""",
		    """no_tpu""",
		    """no_speed""",
		    """no_memory""",
		    """no_env_print""",
		    """no_multi_process""",
		]




		def __init__(     self		,		**_UpperCAmelCase )			-> str:
					for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
											UpperCamelCase_									=     deprecated_arg[3:]
											setattr(self		,		_UpperCAmelCase		,		not kwargs.pop(_UpperCAmelCase ) )
											logger.warning(
											    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
											    f""" {positive_arg}={kwargs[positive_arg]}""" )

					UpperCamelCase_									=     kwargs.pop('torchscript'		,		self.torchscript )
					UpperCamelCase_									=     kwargs.pop('torch_xla_tpu_print_metrics'		,		self.torch_xla_tpu_print_metrics )
					UpperCamelCase_									=     kwargs.pop('fp16_opt_level'		,		self.fpaa_opt_level )
					super().__init__(**_UpperCAmelCase )

		A_						=   field(default=UpperCAmelCase__						,       metadata={"""help""": """Trace the models using torchscript"""}    )
		A_						=   field(default=UpperCAmelCase__						,       metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}    )
		A_						=   field(
		    default="""O1"""						,       metadata={
		        """help""": (
		            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
		            """See details at https://nvidia.github.io/apex/amp.html"""
		        )
		    }						,       )




		@cached_property
		def    _UpperCAmelCase     (     self )			-> Tuple["torch.device", int]:
					requires_backends(self		,		['torch'] )
					logger.info('PyTorch: setting up devices' )
					if not self.cuda:
								UpperCamelCase_									=     torch.device('cpu' )
								UpperCamelCase_									=     0
					elif is_torch_tpu_available():
								UpperCamelCase_									=     xm.xla_device()
								UpperCamelCase_									=     0
					else:
								UpperCamelCase_									=     torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
								UpperCamelCase_									=     torch.cuda.device_count()
					return device, n_gpu




		@property
		def    _UpperCAmelCase     (     self )			-> Dict:
					return is_torch_tpu_available() and self.tpu




		@property
		def    _UpperCAmelCase     (     self )			-> int:
					requires_backends(self		,		['torch'] )
					# TODO(PVP): currently only single GPU is supported
					return torch.cuda.current_device()




		@property
		def    _UpperCAmelCase     (     self )			-> "torch.device":
					requires_backends(self		,		['torch'] )
					return self._setup_devices[0]




		@property
		def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
					requires_backends(self		,		['torch'] )
					return self._setup_devices[1]




		@property
		def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
					return self.n_gpu > 0













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

   return max_revue






def     _snake_case    (__lowercase    ,      __lowercase):
   _enforce_args(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     [float('-inf') for _ in range(n + 1)]
   return _top_down_cut_rod_recursive(__lowercase    ,      __lowercase    ,      __lowercase)






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

      UpperCamelCase_									=     max_revenue

   return max_rev[n]






def     _snake_case    (__lowercase    ,      __lowercase):
   _enforce_args(__lowercase    ,      __lowercase)

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

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

      UpperCamelCase_									=     max_revenue_i

   return max_rev[n]






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

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






def     _snake_case    ():
   UpperCamelCase_									=     [6, 10, 12, 15, 20, 23]
   UpperCamelCase_									=     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.
   UpperCamelCase_									=     36

   UpperCamelCase_									=     top_down_cut_rod(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     bottom_up_cut_rod(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     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()





