"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


lowercase__ 	=     logging.get_logger(__name__)







def 						__a       (     )     ->Optional[Any]:
  # Get the sagemaker specific mp parameters from smp_options variable.
  a__:					Tuple      							=     os.getenv('SM_HP_MP_PARAMETERS'		,					'{}'       )
  try:
    # Parse it and check the field "partitions" is included, it is required for model parallel.
    a__:					Optional[int]      							=     json.loads(_SCREAMING_SNAKE_CASE       )
    if "partitions" not in smp_options:
      return False
  except json.JSONDecodeError:
    return False

  # Get the sagemaker specific framework parameters from mpi_options variable.
  a__:					Optional[int]      							=     os.getenv('SM_FRAMEWORK_PARAMS'		,					'{}'       )
  try:
    # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
    a__:					Any      							=     json.loads(_SCREAMING_SNAKE_CASE       )
    if not mpi_options.get('sagemaker_mpi_enabled'		,					_SCREAMING_SNAKE_CASE       ):
      return False
  except json.JSONDecodeError:
    return False
  # Lastly, check if the `smdistributed` module is present.
  return importlib.util.find_spec('smdistributed'       ) is not None


if is_sagemaker_model_parallel_available():
 import smdistributed.modelparallel.torch as smp

 smp.init()




@dataclass
class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		field(
          default=""""""	,				metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}	,				)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        super().__post_init__()
        warnings.warn(
            '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
            '`TrainingArguments` instead.'   ,      lowercase   ,      )



      @cached_property
      def   lowerCamelCase_       (						self)     ->      "torch.device":





        '''simple docstring'''





        logger.info('PyTorch: setting up devices')
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
          logger.warning(
              'torch.distributed process group is initialized, but local_rank == -1. '
              'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch')
        if self.no_cuda:
          a__:					Union[str, Any]      							=     torch.device('cpu')
          a__:					Union[str, Any]      							=     0
        elif is_sagemaker_model_parallel_available():
          a__:					str      							=     smp.local_rank()
          a__:					Union[str, Any]      							=     torch.device('cuda'   ,      lowercase)
          a__:					Dict      							=     1
        elif is_sagemaker_dp_enabled():
          import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

          torch.distributed.init_process_group(backend='smddp'   ,      timeout=self.ddp_timeout_delta)
          a__:					Tuple      							=     int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'))
          a__:					Tuple      							=     torch.device('cuda'   ,      self.local_rank)
          a__:					int      							=     1
        elif self.local_rank == -1:
          # if n_gpu is > 1 we'll use nn.DataParallel.
          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
          # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
          # trigger an error that a device index is missing. Index 0 takes into account the
          # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
          # will use the first GPU in that env, i.e. GPU#1
          a__:					Optional[int]      							=     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
          # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
          # the default value.
          a__:					List[str]      							=     torch.cuda.device_count()
        else:
          # Here, we'll use torch.distributed.
          # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
          if not torch.distributed.is_initialized():
            torch.distributed.init_process_group(backend='nccl'   ,      timeout=self.ddp_timeout_delta)
          a__:					List[Any]      							=     torch.device('cuda'   ,      self.local_rank)
          a__:					List[str]      							=     1

        if device.type == "cuda":
          torch.cuda.set_device(lowercase)

        return device



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        if is_sagemaker_model_parallel_available():
          return smp.dp_size()

        return super().world_size



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        return not is_sagemaker_model_parallel_available()



      @property
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        return False



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

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

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

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

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  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()


