"""simple docstring"""







import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
     import torch

_SCREAMING_SNAKE_CASE	:		Optional[int]   =   logging.get_logger(__name__)



def       A__    (	)  ->							Dict:
   return importlib.util.find_spec('deepspeed'						) is not None


if is_accelerate_available() and is_deepspeed_available():
     from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
     # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
     # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
     from builtins import object as DeepSpeedConfig






class      _SCREAMING_SNAKE_CASE							(			_UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      set_hf_deepspeed_config(self	)
      dep_version_check('accelerate'	)
      dep_version_check('deepspeed'	)
      super().__init__(lowercase_	)






class      _SCREAMING_SNAKE_CASE							(			_UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      super().__init__(lowercase_	)
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					[]







   def    _A				(	self	):


      '''simple docstring'''

      if self._dtype is None:
         raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'	)
      return self._dtype







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_value(lowercase_	)
      if val is None:
         return False
      else:
         return val == "auto"







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=True	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.find_config_node(lowercase_	)
      if config is None:
         return

      if config.get(lowercase_	) == "auto":
         __lowerCamelCase    		=					hf_val
         return

      if not must_match:
         return

      __lowerCamelCase    		=					config.get(lowercase_	)
      if ds_val is not None and ds_val != hf_val:
         self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''	)

   __UpperCAmelCase	:							Any    =    partialmethod(_UpperCAmelCase    ,				must_match=_UpperCAmelCase				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
      self.fill_match(
          'train_micro_batch_size_per_gpu' ,				args.per_device_train_batch_size ,				'per_device_train_batch_size'	)
      self.fill_match('gradient_accumulation_steps' ,				args.gradient_accumulation_steps ,				'gradient_accumulation_steps'	)
      self.fill_match('train_batch_size' ,				lowercase_ ,				'train_batch_size (calculated)'	)
      self.fill_match('gradient_clipping' ,				args.max_grad_norm ,				'max_grad_norm'	)

      self.fill_match('optimizer.params.lr' ,				args.learning_rate ,				'learning_rate'	)
      self.fill_match('optimizer.params.betas' ,				[args.adam_betaa, args.adam_betaa] ,				'adam_beta1+adam_beta2'	)
      self.fill_match('optimizer.params.eps' ,				args.adam_epsilon ,				'adam_epsilon'	)
      self.fill_match('optimizer.params.weight_decay' ,				args.weight_decay ,				'weight_decay'	)

      self.fill_only('scheduler.params.warmup_min_lr' ,				0	)  # not a trainer arg
      self.fill_match('scheduler.params.warmup_max_lr' ,				args.learning_rate ,				'learning_rate'	)
      # total_num_steps - will get set in trainer_config_finalize

      # fp16
      if args.fpaa or args.fpaa_full_eval:
         __lowerCamelCase    		=					"""apex""" if args.fpaa_backend == """apex""" else """amp"""
      else:
         __lowerCamelCase    		=					None

      if args.save_on_each_node:
         # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
         __lowerCamelCase    		=					self.config.get('checkpoint' ,				{}	)
         __lowerCamelCase    		=					args.save_on_each_node

      # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
      # any here unless the user did the work
      self.fill_match(
          'fp16.enabled' ,				((args.fpaa or args.fpaa_full_eval) and fpaa_backend == 'amp') ,				'fp16|fp16_full_eval+fp16_backend(amp)' ,				)

      # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
      # ZeRO features
      self.fill_match('amp.enabled' ,				fpaa_backend == 'apex' ,				'fp16+fp16_backend(apex)'	)
      self.fill_match('amp.opt_level' ,				args.fpaa_opt_level ,				'fp16_opt_level'	)

      self.fill_match('bf16.enabled' ,				(args.bfaa or args.bfaa_full_eval) ,				'bf16|bf16_full_eval'	)

      # deepspeed's default mode is fp16 unless there is a config that says differently
      if self.is_true('bf16.enabled'	):
         __lowerCamelCase    		=					torch.bfloataa
      elif self.is_false('fp16.enabled'	):
         __lowerCamelCase    		=					torch.floataa
      else:
         __lowerCamelCase    		=					torch.floataa







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					[
          """zero_optimization.reduce_bucket_size""",
          """zero_optimization.stage3_prefetch_bucket_size""",
          """zero_optimization.stage3_param_persistence_threshold""",
      ]
      __lowerCamelCase    		=					[x for x in hidden_size_based_keys if self.is_auto(lowercase_	)]

      if len(lowercase_	) > 0:
         if hasattr(model.config ,				'hidden_size'	):
            __lowerCamelCase    		=					model.config.hidden_size
         elif hasattr(model.config ,				'hidden_sizes'	):
            # if there are many hidden sizes pick the largest one
            __lowerCamelCase    		=					max(model.config.hidden_sizes	)
         else:
            raise ValueError(
                'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
                'therefore it\'s not possible to automatically fill out the following `auto` entries '
                F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                '`auto` values for these keys with an integer value of your choice.'	)

         self.fill_only('zero_optimization.reduce_bucket_size' ,				hidden_size * hidden_size	)
         if self.is_zeroa():
            # automatically assign the optimal config values based on model config
            self.fill_only('zero_optimization.stage3_prefetch_bucket_size' ,				0.9 * hidden_size * hidden_size	)
            self.fill_only('zero_optimization.stage3_param_persistence_threshold' ,				10 * hidden_size	)

        # scheduler
      self.fill_match('scheduler.params.total_num_steps' ,				lowercase_ ,				'num_training_steps (calculated)'	)
      self.fill_match('scheduler.params.warmup_num_steps' ,				args.get_warmup_steps(lowercase_	) ,				'warmup_steps'	)

      if len(self.mismatches	) > 0:
         __lowerCamelCase    		=					"""\n""".join(self.mismatches	)
         raise ValueError(
             'Please correct the following DeepSpeed config values that mismatch TrainingArguments'
             F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''	)

_SCREAMING_SNAKE_CASE	:		int   =   None



def       A__    (	lowercase_     :	Tuple						)  ->							Any:
   # this is a special weakref global object to allow us to get to Deepspeed config from APIs
   # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
   global _hf_deepspeed_config_weak_ref
   # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
   __lowerCamelCase    		=					weakref.ref(UpperCAmelCase__						)



def       A__    (	)  ->							Optional[int]:
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   __lowerCamelCase    		=					None



def       A__    (	)  ->							Any:
   if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
      return _hf_deepspeed_config_weak_ref().is_zeroa()
   else:
      return False



def       A__    (	)  ->							Tuple:
   if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
      return _hf_deepspeed_config_weak_ref().config
   else:
      return None



def       A__    (	lowercase_     :	Dict      , lowercase_     :	Optional[int]      , lowercase_     :	Tuple      , lowercase_     :	Optional[Any]      , lowercase_     :	Optional[Any]						)  ->							Any:
   from accelerate.utils import DummyOptim, DummyScheduler

   __lowerCamelCase    		=					hf_deepspeed_config.config

   # Optimizer + Scheduler
   # Currently supported combos:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Yes
   # 3. DS scheduler + HF optimizer: Yes
   # 4. HF scheduler + DS optimizer: No
   #
   # Unless Offload is enabled in which case it's:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Mostly*
   # 3. DS scheduler + HF optimizer: Mostly*
   # 4. HF scheduler + DS optimizer: No
   #
   # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

   __lowerCamelCase    		=					None
   if "optimizer" in config:
      if args.adafactor:
         raise ValueError(
             '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
             'Only one optimizer can be configured.'						)
      __lowerCamelCase    		=					DummyOptim(params=UpperCAmelCase__						)
   else:
      if hf_deepspeed_config.is_offload():
         logger.info(
             'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
             ' custom optimizer has both CPU and GPU implementation (except LAMB)'						)

      # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
      # But trainer uses AdamW by default.
      __lowerCamelCase    		=					trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      __lowerCamelCase    		=					True

   __lowerCamelCase    		=					None
   if "scheduler" in config:
      __lowerCamelCase    		=					DummyScheduler(UpperCAmelCase__						)
   else:
      if isinstance(UpperCAmelCase__      , UpperCAmelCase__						):
         raise ValueError(
             'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
             'Please configure a scheduler in the DeepSpeed config.'						)
      __lowerCamelCase    		=					trainer.create_scheduler(num_training_steps=UpperCAmelCase__      , optimizer=UpperCAmelCase__						)

   return optimizer, lr_scheduler



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Tuple      , lowercase_     :	int=False						)  ->							int:
   from deepspeed.utils import logger as ds_logger

   __lowerCamelCase    		=					trainer.model
   __lowerCamelCase    		=					trainer.args

   __lowerCamelCase    		=					trainer.accelerator.state.deepspeed_plugin.hf_ds_config

   # resume config update - some bits like `model` and `num_training_steps` only become available during train
   hf_deepspeed_config.trainer_config_finalize(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__						)

   # set the Deepspeed log level consistent with the Trainer
   ds_logger.setLevel(args.get_process_log_level()						)

   if inference:
      # only Z3 makes sense for the inference
      if not hf_deepspeed_config.is_zeroa():
         raise ValueError('ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'						)

      # in case the training config is re-used for inference
      hf_deepspeed_config.del_config_sub_tree('optimizer'						)
      hf_deepspeed_config.del_config_sub_tree('lr_scheduler'						)
      __lowerCamelCase    		=					None, None
      __lowerCamelCase    		=					None
   else:
      __lowerCamelCase    		=					None  # important for when deepspeed_init is used as re-init
      __lowerCamelCase    		=					list(filter(lambda lowercase_						: p.requires_grad      , model.parameters()						)						)
      __lowerCamelCase    		=					deepspeed_optim_sched(
          UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__						)

   # keep for quick debug:
   # from pprint import pprint; pprint(config)

   return optimizer, lr_scheduler



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Tuple						)  ->							List[Any]:
   # it's possible that the user is trying to resume from model_path, which doesn't necessarily
   # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
   # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
   # path contains what looks like a deepspeed checkpoint
   import glob

   __lowerCamelCase    		=					sorted(glob.glob(F'''{checkpoint_path}/global_step*'''						)						)

   if len(UpperCAmelCase__						) > 0:
      logger.info(F'''Attempting to resume from {checkpoint_path}'''						)
      # this magically updates self.optimizer and self.lr_scheduler
      __lowerCamelCase    		=					deepspeed_engine.load_checkpoint(
          UpperCAmelCase__      , load_optimizer_states=UpperCAmelCase__      , load_lr_scheduler_states=UpperCAmelCase__						)
      if load_path is None:
         raise ValueError(F'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''						)
   else:
      raise ValueError(F'''Can\'t find a valid checkpoint at {checkpoint_path}'''						)







'''simple docstring'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





