


"""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			=							logging.get_logger(__name__)




def        snake_case_					(  )  ->    Optional[int]:
       # Get the sagemaker specific mp parameters from smp_options variable.
       lowercase__:       Tuple        =       os.getenv('SM_HP_MP_PARAMETERS'      ,							'{}'					)
       try:
              # Parse it and check the field "partitions" is included, it is required for model parallel.
              lowercase__:       str        =       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.
       lowercase__:       Dict        =       os.getenv('SM_FRAMEWORK_PARAMS'      ,							'{}'					)
       try:
              # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
              lowercase__:       int        =       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   __a      (   __UpperCamelCase				):
 __lowercase			:							str       					=			field(
     default=''	,	metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}	,	)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''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			) ->    "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:
               lowercase__:       Union[str, Any]        =       torch.device('cpu'			)
               lowercase__:       List[str]        =       0
        elif is_sagemaker_model_parallel_available():
               lowercase__:       Optional[Any]        =       smp.local_rank()
               lowercase__:       List[Any]        =       torch.device('cuda'    ,			lowerCAmelCase__			)
               lowercase__:       str        =       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			)
               lowercase__:       Dict        =       int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'			)			)
               lowercase__:       Dict        =       torch.device('cuda'    ,			self.local_rank			)
               lowercase__:       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
               lowercase__:       Dict        =       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.
               lowercase__:       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			)
               lowercase__:       Optional[int]        =       torch.device('cuda'    ,			self.local_rank			)
               lowercase__:       Optional[int]        =       1

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

        return device
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




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

        return super().world_size
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return not is_sagemaker_model_parallel_available()
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return False






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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


