
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


UpperCAmelCase       =					logging.get_logger(__name__)






def 				UpperCAmelCase_						(				):
	# Get the sagemaker specific mp parameters from smp_options variable.
	lowercase	    =       os.getenv('SM_HP_MP_PARAMETERS'					,				'{}'				)
	try:
		# Parse it and check the field "partitions" is included, it is required for model parallel.
		lowercase	    =       json.loads(__SCREAMING_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	    =       os.getenv('SM_FRAMEWORK_PARAMS'					,				'{}'				)
	try:
		# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
		lowercase	    =       json.loads(__SCREAMING_SNAKE_CASE				)
		if not mpi_options.get('sagemaker_mpi_enabled'					,				__SCREAMING_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_  (     __lowerCamelCase  ):




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().__post_init__()
		warnings.warn(
		    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
		    '`TrainingArguments` instead.'						,       snake_case						,       )





	@cached_property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		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	    =       torch.device('cpu'			)
			lowercase	    =       0
		elif is_sagemaker_model_parallel_available():
			lowercase	    =       smp.local_rank()
			lowercase	    =       torch.device('cuda'						,       snake_case			)
			lowercase	    =       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	    =       int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'			)			)
			lowercase	    =       torch.device('cuda'						,       self.local_rank			)
			lowercase	    =       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	    =       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	    =       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	    =       torch.device('cuda'						,       self.local_rank			)
			lowercase	    =       1

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

		return device





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if is_sagemaker_model_parallel_available():
			return smp.dp_size()

		return super().world_size





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return not is_sagemaker_model_parallel_available()





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return False







'''simple docstring'''


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
				_snake_case : Tuple  				=						{
				    'linear': PIL.Image.Resampling.BILINEAR,
				    'bilinear': PIL.Image.Resampling.BILINEAR,
				    'bicubic': PIL.Image.Resampling.BICUBIC,
				    'lanczos': PIL.Image.Resampling.LANCZOS,
				    'nearest': PIL.Image.Resampling.NEAREST,
				}
else:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

