



"""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


_snake_case          =							logging.get_logger(__name__)



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      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(_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__           =		os.getenv('SM_FRAMEWORK_PARAMS'		,   '{}'							)
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            lowerCamelCase__           =		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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				str             =    field(
        default=''		,		metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}		,		)
    def      _UpperCamelCase       ( self							:					Tuple				):
          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							:					Tuple				):
          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'							,					UpperCAmelCase__				)
                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(UpperCAmelCase__				)

          return device
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          if is_sagemaker_model_parallel_available():
                return smp.dp_size()

          return super().world_size
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          return not is_sagemaker_model_parallel_available()


    @property
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          return False







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



