
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 			lowerCamelCase_ (				):
				# Get the sagemaker specific mp parameters from smp_options variable.
				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(lowerCamelCase__    )
								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(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     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = field(
	    default=''			,       metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,       )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					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     SCREAMING_SNAKE_CASE_(    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:
									lowerCamelCase_						=			torch.device("cpu" )
									lowerCamelCase_						=			0
					elif is_sagemaker_model_parallel_available():
									lowerCamelCase_						=			smp.local_rank()
									lowerCamelCase_						=			torch.device("cuda"						,  lowercase )
									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(lowercase )

					return device



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return not is_sagemaker_model_parallel_available()



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return False




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


