



"""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


_lowerCAmelCase    :List[str]     =					logging.get_logger(__name__)





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

    # Get the sagemaker specific framework parameters from mpi_options variable.
    _UpperCAmelCase							:  Optional[int]											=    os.getenv('''SM_FRAMEWORK_PARAMS'''     ,       '''{}'''							)
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        _UpperCAmelCase							:  int											=    json.loads(UpperCamelCase__							)
        if not mpi_options.get('''sagemaker_mpi_enabled'''     ,       UpperCamelCase__							):
            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							( a      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         super().__post_init__()
         warnings.warn(
             '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
             '''`TrainingArguments` instead.'''       ,	A       ,	)





     @cached_property
     def  __lowerCAmelCase	(					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:
             _UpperCAmelCase							:  Dict											=    torch.device('''cpu'''		)
             _UpperCAmelCase							:  Union[str, Any]											=    0
         elif is_sagemaker_model_parallel_available():
             _UpperCAmelCase							:  Optional[Any]											=    smp.local_rank()
             _UpperCAmelCase							:  str											=    torch.device('''cuda'''       ,	A		)
             _UpperCAmelCase							:  Optional[int]											=    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							:  Optional[int]											=    int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''		)		)
             _UpperCAmelCase							:  List[str]											=    torch.device('''cuda'''       ,	self.local_rank		)
             _UpperCAmelCase							:  Optional[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
             _UpperCAmelCase							:  List[str]											=    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[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		)
             _UpperCAmelCase							:  str											=    torch.device('''cuda'''       ,	self.local_rank		)
             _UpperCAmelCase							:  Union[str, Any]											=    1

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

         return device





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         if is_sagemaker_model_parallel_available():
             return smp.dp_size()

         return super().world_size





     @property
     def  __lowerCAmelCase	(					self		)							->					str:
         return not is_sagemaker_model_parallel_available()





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







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


