




'''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


a  :				List[Any]									=  logging.get_logger(__name__)


def 		__magic_name__    (	)		->					str:

					'''simple docstring'''

					snake_case_									=				os.getenv('''SM_HP_MP_PARAMETERS''',      '''{}'''	)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										snake_case_									=				json.loads(__UpperCAmelCase	)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					snake_case_									=				os.getenv('''SM_FRAMEWORK_PARAMS''',      '''{}'''	)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										snake_case_									=				json.loads(__UpperCAmelCase	)
										if not mpi_options.get('''sagemaker_mpi_enabled''',      __UpperCAmelCase	):
															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     ):
				snake_case_      =		field(
				    default=""   ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}   ,     )


				def   A_ (		self    :					Optional[Any]					):
									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   A_ (		self    :					List[Any]					):
									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:
														snake_case_									=				torch.device('''cpu'''					)
														snake_case_									=				0
									elif is_sagemaker_model_parallel_available():
														snake_case_									=				smp.local_rank()
														snake_case_									=				torch.device('''cuda'''       ,				lowercase_					)
														snake_case_									=				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					)
														snake_case_									=				int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''					)					)
														snake_case_									=				torch.device('''cuda'''       ,				self.local_rank					)
														snake_case_									=				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
														snake_case_									=				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.
														snake_case_									=				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					)
														snake_case_									=				torch.device('''cuda'''       ,				self.local_rank					)
														snake_case_									=				1

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

									return device


				@property
				def   A_ (		self    :					Optional[int]					):
									if is_sagemaker_model_parallel_available():
														return smp.dp_size()

									return super().world_size


				@property
				def   A_ (		self    :					Dict					):
									return not is_sagemaker_model_parallel_available()


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









"""simple docstring"""




from __future__ import annotations

import time

import numpy as np

_snake_case          =   [8, 5, 9, 7]
_snake_case          =   [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_snake_case          =   [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class        UpperCamelCase			:


 def __init__(     self		:						List[Any]							,						UpperCAmelCase__		:						list[int]							,						UpperCAmelCase__		:						list[list[int]]							,						UpperCAmelCase__		:						list[list[int]]							,						)					->       None:
   _a  :       List[str]             =	claim_vector
   _a  :       List[Any]             =	allocated_resources_table
   _a  :       Union[str, Any]             =	maximum_claim_table


 def _lowercase	(     self		:						Tuple							)					->       list[int]:
   return [
       sum(p_item[i] for p_item in self.__allocated_resources_table							)
       for i in range(len(self.__allocated_resources_table[0]							)							)
   ]


 def _lowercase	(     self		:						int							)					->       list[int]:
   return np.array(self.__claim_vector							) - np.array(
       self.__processes_resource_summation()							)


 def _lowercase	(     self		:						List[str]							)					->       list[list[int]]:
   return [
       list(np.array(self.__maximum_claim_table[i]							) - np.array(UpperCAmelCase__							)							)
       for i, allocated_resource in enumerate(self.__allocated_resources_table							)
   ]


 def _lowercase	(     self		:						Optional[Any]							)					->       dict[int, list[int]]:
   return {self.__need().index(UpperCAmelCase__							): i for i in self.__need()}


 def _lowercase	(     self		:						Dict							,						**UpperCAmelCase__		:						Optional[Any]							)					->       None:
   _a  :       List[Any]             =	self.__need()
   _a  :       Optional[int]             =	self.__allocated_resources_table
   _a  :       str             =	self.__available_resources()
   _a  :       Optional[Any]             =	self.__need_index_manager()
   for kw, val in kwargs.items():
     if kw and val is True:
       self.__pretty_data()
   print("""_""" * 50 + """\n"""							)
   while need_list:
     _a  :       int             =	False
     for each_need in need_list:
       _a  :       Optional[int]             =	True
       for index, need in enumerate(UpperCAmelCase__							):
         if need > available_resources[index]:
           _a  :       List[Any]             =	False
           break
       if execution:
         _a  :       str             =	True
         # get the original index of the process from ind_ctrl db
         for original_need_index, need_clone in need_index_manager.items():
           if each_need == need_clone:
             _a  :       Any             =	original_need_index
         print(f"""Process {process_number + 1} is executing."""							)
         # remove the process run from stack
         need_list.remove(UpperCAmelCase__							)
         # update available/freed resources stack
         _a  :       Union[str, Any]             =	np.array(UpperCAmelCase__							) + np.array(
             alloc_resources_table[process_number]							)
         print(
             """Updated available resource stack for processes: """
             + """ """.join([str(UpperCAmelCase__							) for x in available_resources]							)							)
         break
     if safe:
       print("""The process is in a safe state.\n"""							)
     else:
       print("""System in unsafe state. Aborting...\n"""							)
       break







 def _lowercase	(     self		:						Any							)					->       Optional[int]:
   print(""" """ * 9 + """Allocated Resource Table"""							)
   for item in self.__allocated_resources_table:
     print(
         f"""P{self.__allocated_resources_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(""" """ * 9 + """System Resource Table"""							)
   for item in self.__maximum_claim_table:
     print(
         f"""P{self.__maximum_claim_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(
       """Current Usage by Active Processes: """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__claim_vector							)							)
   print(
       """Initial Available Resources:       """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__available_resources()							)							)
   time.sleep(1							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

