'''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


_UpperCAmelCase    :     Tuple         =       logging.get_logger(__name__)





def  _SCREAMING_SNAKE_CASE		(      ):
     # Get the sagemaker specific mp parameters from smp_options variable.
     _A    		=  os.getenv('SM_HP_MP_PARAMETERS'   ,		'{}'				)
     try:
          # Parse it and check the field "partitions" is included, it is required for model parallel.
          _A    		=  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.
     _A    		=  os.getenv('SM_FRAMEWORK_PARAMS'   ,		'{}'				)
     try:
          # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
          _A    		=  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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default=""    ,    metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}    ,    )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[Any]:
        super().__post_init__()
        warnings.warn(
            '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
            '`TrainingArguments` instead.', UpperCamelCase__, )
   @cached_property
   def 	__UpperCAmelCase   (      self      :			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:
             _A    		=  torch.device('cpu'     )
             _A    		=  0
        elif is_sagemaker_model_parallel_available():
             _A    		=  smp.local_rank()
             _A    		=  torch.device('cuda', UpperCamelCase__     )
             _A    		=  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    		=  int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'     )     )
             _A    		=  torch.device('cuda', self.local_rank     )
             _A    		=  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    		=  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    		=  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    		=  torch.device('cuda', self.local_rank     )
             _A    		=  1

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

        return device
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        if is_sagemaker_model_parallel_available():
             return smp.dp_size()

        return super().world_size
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        return not is_sagemaker_model_parallel_available()

   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        return False







from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




