'''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__     :	str				=			logging.get_logger(__name__)







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  os.getenv('''SM_HP_MP_PARAMETERS'''  ,			'''{}'''    )
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      UpperCAmelCase__							:				List[str]   				=  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.
   UpperCAmelCase__							:				int   				=  os.getenv('''SM_FRAMEWORK_PARAMS'''  ,			'''{}'''    )
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      UpperCAmelCase__							:				Any   				=  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 				_lowercase	(      __lowercase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   field(
          default=''''''		,metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}		,)




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         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 		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:
            UpperCAmelCase__							:				Optional[int]   				=  torch.device('''cpu'''							)
            UpperCAmelCase__							:				List[str]   				=  0
         elif is_sagemaker_model_parallel_available():
            UpperCAmelCase__							:				Any   				=  smp.local_rank()
            UpperCAmelCase__							:				int   				=  torch.device('''cuda'''					,lowerCamelCase_							)
            UpperCAmelCase__							:				List[Any]   				=  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							)
            UpperCAmelCase__							:				Tuple   				=  int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''							)							)
            UpperCAmelCase__							:				Union[str, Any]   				=  torch.device('''cuda'''					,self.local_rank							)
            UpperCAmelCase__							:				Tuple   				=  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
            UpperCAmelCase__							:				Optional[Any]   				=  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.
            UpperCAmelCase__							:				List[Any]   				=  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							)
            UpperCAmelCase__							:				Optional[Any]   				=  torch.device('''cuda'''					,self.local_rank							)
            UpperCAmelCase__							:				List[str]   				=  1

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

         return device




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



         '''simple docstring'''



         if is_sagemaker_model_parallel_available():
            return smp.dp_size()

         return super().world_size




      @property
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         return not is_sagemaker_model_parallel_available()




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



         '''simple docstring'''



         return False





'''simple docstring'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




