
"""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		       =							logging.get_logger(__name__)







def 	__a     (		):
   # 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_					:					Dict			     =    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_					:					Optional[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 A_	(lowercase__      ):




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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().__post_init__()
     warnings.warn(
         "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
         "`TrainingArguments` instead."				,   lowercase_				,   )







  @cached_property
  def        UpperCamelCase__					(		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:
        UpperCAmelCase_					:					Any			     =    torch.device("cpu"  )
        UpperCAmelCase_					:					Any			     =    0
     elif is_sagemaker_model_parallel_available():
        UpperCAmelCase_					:					int			     =    smp.local_rank()
        UpperCAmelCase_					:					Union[str, Any]			     =    torch.device("cuda"				,   lowercase_  )
        UpperCAmelCase_					:					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_					:					List[str]			     =    int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"  )  )
        UpperCAmelCase_					:					Union[str, Any]			     =    torch.device("cuda"				,   self.local_rank  )
        UpperCAmelCase_					:					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_					:					Tuple			     =    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_					:					List[str]			     =    torch.device("cuda"				,   self.local_rank  )
        UpperCAmelCase_					:					int			     =    1

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

     return device







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return not is_sagemaker_model_parallel_available()







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return False







'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')