



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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			)		->							Optional[Any]:
		# Get the sagemaker specific mp parameters from smp_options variable.
		UpperCAmelCase_						=				os.getenv("SM_HP_MP_PARAMETERS"			,     "{}" )
		try:
				# Parse it and check the field "partitions" is included, it is required for model parallel.
				UpperCAmelCase_						=				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_						=				os.getenv("SM_FRAMEWORK_PARAMS"			,     "{}" )
		try:
				# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
				UpperCAmelCase_						=				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 	lowerCamelCase      (						__lowerCamelCase  ):







					'''simple docstring'''







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



					def    A__		(			self     ):
							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    A__		(			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:
									UpperCAmelCase_						=				torch.device("cpu"     )
									UpperCAmelCase_						=				0
							elif is_sagemaker_model_parallel_available():
									UpperCAmelCase_						=				smp.local_rank()
									UpperCAmelCase_						=				torch.device("cuda"    ,	UpperCAmelCase_     )
									UpperCAmelCase_						=				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_						=				int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"     )     )
									UpperCAmelCase_						=				torch.device("cuda"    ,	self.local_rank     )
									UpperCAmelCase_						=				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_						=				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_						=				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_						=				torch.device("cuda"    ,	self.local_rank     )
									UpperCAmelCase_						=				1

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

							return device



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

							return super().world_size



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



					@property
					def    A__		(			self     ):
							return False






from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 768    ,	):
							super().__init__()

							UpperCAmelCase_						=				nn.Parameter(torch.zeros(1    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(1    ,	lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	):
							UpperCAmelCase_						=				nn.Parameter(self.mean.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(self.std.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							return self



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds - self.mean) * 1.0 / self.std
							return embeds



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds * self.std) + self.mean
							return embeds

