




'''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


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





def      a_							(      )							->						Union[str, Any]:



       """simple docstring"""
       # 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(__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.
       lowerCamelCase_							=os.getenv('''SM_FRAMEWORK_PARAMS'''   ,       '''{}'''   )
       try:
              # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
              lowerCamelCase_							=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   __UpperCamelCase       ( lowerCamelCase__				):
       lowercase	:    str      						=field(
           default=''			,					metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,					)






       def      lowercase__     (							self  ):



              """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      lowercase__     (							self  ):



              """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_							=torch.device('''cpu'''  )
                     lowerCamelCase_							=0
              elif is_sagemaker_model_parallel_available():
                     lowerCamelCase_							=smp.local_rank()
                     lowerCamelCase_							=torch.device('''cuda''',		lowerCAmelCase  )
                     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(lowerCAmelCase  )

              return device






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              if is_sagemaker_model_parallel_available():
                     return smp.dp_size()

              return super().world_size






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return not is_sagemaker_model_parallel_available()






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return False











'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
