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_     :   Optional[Any] 				=				logging.get_logger(__name__)







def 			A__      (	):
 # Get the sagemaker specific mp parameters from smp_options variable.
 SCREAMING_SNAKE_CASE__:   List[str]=      os.getenv('''SM_HP_MP_PARAMETERS'''       ,				'''{}'''      )
 try:
  # Parse it and check the field "partitions" is included, it is required for model parallel.
  SCREAMING_SNAKE_CASE__:   int=      json.loads(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.
 SCREAMING_SNAKE_CASE__:   int=      os.getenv('''SM_FRAMEWORK_PARAMS'''       ,				'''{}'''      )
 try:
  # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
  SCREAMING_SNAKE_CASE__:   List[Any]=      json.loads(snake_case_      )
  if not mpi_options.get('''sagemaker_mpi_enabled'''       ,				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 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      field(
        default=""    ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}    ,     )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     super().__post_init__()
     warnings.warn(
         '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
         '''`TrainingArguments` instead.''' , lowerCAmelCase , )
    @cached_property
    def 						UpperCamelCase_				(  self   )       ->					"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:
      SCREAMING_SNAKE_CASE__:   Any=      torch.device('''cpu'''   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      0
     elif is_sagemaker_model_parallel_available():
      SCREAMING_SNAKE_CASE__:   List[str]=      smp.local_rank()
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.device('''cuda''' , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      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   )
      SCREAMING_SNAKE_CASE__:   List[str]=      int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''   )   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      torch.device('''cuda''' , self.local_rank   )
      SCREAMING_SNAKE_CASE__:   Any=      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
      SCREAMING_SNAKE_CASE__:   Tuple=      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.
      SCREAMING_SNAKE_CASE__:   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   )
      SCREAMING_SNAKE_CASE__:   str=      torch.device('''cuda''' , self.local_rank   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      1

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

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

     return super().world_size
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return not is_sagemaker_model_parallel_available()



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


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowercase_     :   str 				=				re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      None
    __a  =      None
    __a  =      None
    __a  =      None
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      _str_to_version_tuple(self.version_str   )
    def __repr__(  self   )       ->					Dict:
     return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return self.major, self.minor, self.patch
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return Version(lowerCAmelCase   )
     elif isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return other
     raise TypeError(f'{other} (type {type(lowerCAmelCase   )}) cannot be compared to version.'   )
    def __eq__(  self , lowerCAmelCase   )       ->					Optional[int]:
     try:
      SCREAMING_SNAKE_CASE__:   List[str]=      self._validate_operand(lowerCAmelCase   )
     except (TypeError, ValueError):
      return False
     else:
      return self.tuple == other.tuple
    def __lt__(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._validate_operand(lowerCAmelCase   )
     return self.tuple < other.tuple
    def __hash__(  self   )       ->					List[Any]:
     return hash(_version_tuple_to_str(self.tuple   )   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      {f.name for f in dataclasses.fields(cls   )}
     return cls(**{k: v for k, v in dic.items() if k in field_names}   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     return self.version_str







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   List[Any]=      _VERSION_REG.match(snake_case_      )
 if not res:
  raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
 return tuple(int(snake_case_      ) for v in [res.group('''major'''      ), res.group('''minor'''      ), res.group('''patch'''      )]      )







def 			A__      (	snake_case_ :		Union[str, Any]      ):
 return ".".join(str(snake_case_      ) for v in version_tuple      )

