

def      snake_case_				(__A					:						int ,       __A					:						int					)      ->     int:
   return int((input_a, input_a).count(0					) == 0					)





def      snake_case_				()      ->     None:
   assert and_gate(0 ,       0					) == 0
   assert and_gate(0 ,       1					) == 0
   assert and_gate(1 ,       0					) == 0
   assert and_gate(1 ,       1					) == 1


if __name__ == "__main__":
   test_and_gate()
   print(and_gate(1, 0))
   print(and_gate(0, 0))
   print(and_gate(0, 1))
   print(and_gate(1, 1))





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								=			logging.get_logger(__name__)





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

   # Get the sagemaker specific framework parameters from mpi_options variable.
   __lowerCAmelCase  :      Any    =						os.getenv("""SM_FRAMEWORK_PARAMS""" ,       """{}"""					)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      __lowerCAmelCase  :      Union[str, Any]    =						json.loads(__A					)
      if not mpi_options.get("""sagemaker_mpi_enabled""" ,       __A					):
         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 SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     "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:
        __lowerCAmelCase  :      Optional[Any]    =						torch.device("""cpu"""   )
        __lowerCAmelCase  :      Union[str, Any]    =						0
     elif is_sagemaker_model_parallel_available():
        __lowerCAmelCase  :      int    =						smp.local_rank()
        __lowerCAmelCase  :      str    =						torch.device("""cuda"""	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						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  :      List[str]    =						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""   )   )
        __lowerCAmelCase  :      Tuple    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      Optional[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
        __lowerCAmelCase  :      List[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.
        __lowerCAmelCase  :      Optional[int]    =						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  :      List[str]    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      int    =						1

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

     return device







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return not is_sagemaker_model_parallel_available()







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     return False


