






"""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


_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						logging.get_logger(__name__)







def      lowerCamelCase__       (							)      ->				str:
  # Get the sagemaker specific mp parameters from smp_options variable.
  lowerCamelCase_         =						os.getenv('SM_HP_MP_PARAMETERS'		,				'{}' )
  try:
    # Parse it and check the field "partitions" is included, it is required for model parallel.
    lowerCamelCase_         =						json.loads(_lowerCamelCase )
    if "partitions" not in smp_options:
      return False
  except json.JSONDecodeError:
    return False

  # Get the sagemaker specific framework parameters from mpi_options variable.
  lowerCamelCase_         =						os.getenv('SM_FRAMEWORK_PARAMS'		,				'{}' )
  try:
    # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
    lowerCamelCase_         =						json.loads(_lowerCamelCase )
    if not mpi_options.get('sagemaker_mpi_enabled'		,				_lowerCamelCase ):
      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      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     str						= field(
        default=""""""  ,      metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}  ,      )






    def    UpperCamelCase       (       self     :							int					)     ->		str:
      super().__post_init__()
      warnings.warn(
          '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
          '`TrainingArguments` instead.'       ,						__SCREAMING_SNAKE_CASE       ,						)






    @cached_property
    def    UpperCamelCase       (       self     :							List[str]					)     ->		"torch.device":
      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:
        lowerCamelCase_         =						torch.device('cpu'					)
        lowerCamelCase_         =						0
      elif is_sagemaker_model_parallel_available():
        lowerCamelCase_         =						smp.local_rank()
        lowerCamelCase_         =						torch.device('cuda'       ,						__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						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					)
        lowerCamelCase_         =						int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'					)					)
        lowerCamelCase_         =						torch.device('cuda'       ,						self.local_rank					)
        lowerCamelCase_         =						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
        lowerCamelCase_         =						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.
        lowerCamelCase_         =						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					)
        lowerCamelCase_         =						torch.device('cuda'       ,						self.local_rank					)
        lowerCamelCase_         =						1

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

      return device






    @property
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Any:
      if is_sagemaker_model_parallel_available():
        return smp.dp_size()

      return super().world_size






    @property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		str:
      return not is_sagemaker_model_parallel_available()






    @property
    def    UpperCamelCase       (       self     :							Tuple					)     ->		int:
      return False












"""simple docstring"""





from math import pow, sqrt







def      lowerCamelCase__       (							*_lowerCamelCase       :		float )      ->				bool:
  lowerCamelCase_         =						len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase )
      else ValueError('Input Error: Molar mass values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,				2 )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,				2 ) / molar_mass		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )




