






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

	def      __UpperCamelCase							(      self   :						str):
			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      __UpperCamelCase							(      self   :						Dict):
			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__				:					Tuple					=					torch.device('cpu')
					UpperCamelCase__				:					Tuple					=					0
			elif is_sagemaker_model_parallel_available():
					UpperCamelCase__				:					str					=					smp.local_rank()
					UpperCamelCase__				:					List[Any]					=					torch.device('cuda'  ,					UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					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__				:					Dict					=					int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'))
					UpperCamelCase__				:					List[Any]					=					torch.device('cuda'  ,					self.local_rank)
					UpperCamelCase__				:					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
					UpperCamelCase__				:					str					=					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__				:					Optional[int]					=					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__				:					Dict					=					torch.device('cuda'  ,					self.local_rank)
					UpperCamelCase__				:					List[Any]					=					1

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

			return device

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

			return super().world_size

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			return not is_sagemaker_model_parallel_available()





	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			return False












'''simple docstring'''




import requests
from bsa import BeautifulSoup







def       __UpperCAmelCase						(  lowerCamelCase_ = "AAPL")							->     str:
		UpperCamelCase__				:					str					=					f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
		UpperCamelCase__				:					Optional[Any]					=					BeautifulSoup(requests.get(lowerCamelCase_).text     ,      'html.parser')
		UpperCamelCase__				:					Union[str, Any]					=					'My(6px) Pos(r) smartphone_Mt(6px)'
		return soup.find('div'     ,      class_=class_).find('span').text


if __name__ == "__main__":
		for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
				print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')




