





"""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

__A :		Dict      							=    logging.get_logger(__name__)
def 					A_			(		):







    '''simple docstring'''
    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 							lowerCamelCase		( lowercase__				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # set global weakref object
       set_hf_deepspeed_config(self )
       dep_version_check("""accelerate""" )
       dep_version_check("""deepspeed""" )
       super().__init__(_A )







class 							lowerCamelCase		( lowercase__				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       super().__init__(_A )
       UpperCamelCase				:   List[str]          =							None
       UpperCamelCase				:   List[Any]          =							[]







   def       a_							(							self ):
       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_ ):
       UpperCamelCase				:   Any          =							self.get_value(_A )
       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 ):
       UpperCamelCase				:   Tuple          =							self.find_config_node(_A )
       if config is None:
           return

       if config.get(_A ) == "auto":
           UpperCamelCase				:   Dict          =							hf_val
           return

       if not must_match:
           return

       UpperCamelCase				:   Union[str, Any]          =							config.get(_A )
       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}' )

   lowercase       :  Dict      =				partialmethod(lowercase__	,  must_match=lowercase__				)







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   str          =							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"""    ,      _A    ,      """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:
           UpperCamelCase				:   Tuple          =							'apex' if args.fpaa_backend == 'apex' else 'amp'
       else:
           UpperCamelCase				:   int          =							None

       if args.save_on_each_node:
           # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
           UpperCamelCase				:   Union[str, Any]          =							self.config.get("""checkpoint"""    ,      {} )
           UpperCamelCase				:   Tuple          =							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""" ):
           UpperCamelCase				:   Optional[int]          =							torch.bfloataa
       elif self.is_false("""fp16.enabled""" ):
           UpperCamelCase				:   Optional[int]          =							torch.floataa
       else:
           UpperCamelCase				:   Optional[int]          =							torch.floataa







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Any          =							[
           'zero_optimization.reduce_bucket_size',
           'zero_optimization.stage3_prefetch_bucket_size',
           'zero_optimization.stage3_param_persistence_threshold',
       ]
       UpperCamelCase				:   Dict          =							[x for x in hidden_size_based_keys if self.is_auto(_A )]

       if len(_A ) > 0:
           if hasattr(model.config    ,      """hidden_size""" ):
               UpperCamelCase				:   Any          =							model.config.hidden_size
           elif hasattr(model.config    ,      """hidden_sizes""" ):
               # if there are many hidden sizes pick the largest one
               UpperCamelCase				:   Dict          =							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"""    ,      _A    ,      """num_training_steps (calculated)""" )
       self.fill_match("""scheduler.params.warmup_num_steps"""    ,      args.get_warmup_steps(_A )    ,      """warmup_steps""" )

       if len(self.mismatches ) > 0:
           UpperCamelCase				:   str          =							'\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\'.' )







__A :		Optional[Any]      							=    None
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    UpperCamelCase				:   int          =							weakref.ref(snake_case_    )
def 					A_			(		):







    '''simple docstring'''
    global _hf_deepspeed_config_weak_ref
    UpperCamelCase				:   Optional[Any]          =							None
def 					A_			(		):







    '''simple docstring'''







    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_			(		):







    '''simple docstring'''







    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_			(		snake_case_     :       Optional[int]						,snake_case_     :       str						,snake_case_     :       Optional[Any]						,snake_case_     :       Optional[Any]						,snake_case_     :       str    ):







    '''simple docstring'''
    from accelerate.utils import DummyOptim, DummyScheduler

    UpperCamelCase				:   Dict          =							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)

    UpperCamelCase				:   List[str]          =							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."""    )
        UpperCamelCase				:   Optional[int]          =							DummyOptim(params=snake_case_    )
    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.
        UpperCamelCase				:   Union[str, Any]          =							trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        UpperCamelCase				:   List[Any]          =							True

    UpperCamelCase				:   Optional[int]          =							None
    if "scheduler" in config:
        UpperCamelCase				:   str          =							DummyScheduler(snake_case_    )
    else:
        if isinstance(snake_case_						,snake_case_    ):
            raise ValueError(
                """Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. """
                """Please configure a scheduler in the DeepSpeed config."""    )
        UpperCamelCase				:   Optional[int]          =							trainer.create_scheduler(num_training_steps=snake_case_						,optimizer=snake_case_    )

    return optimizer, lr_scheduler
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Optional[Any]						,snake_case_     :       Dict=False    ):







    '''simple docstring'''
    from deepspeed.utils import logger as ds_logger

    UpperCamelCase				:   Union[str, Any]          =							trainer.model
    UpperCamelCase				:   Dict          =							trainer.args

    UpperCamelCase				:   List[Any]          =							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(snake_case_						,snake_case_						,snake_case_    )

    # 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"""    )
        UpperCamelCase				:   str          =							None, None
        UpperCamelCase				:   List[Any]          =							None
    else:
        UpperCamelCase				:   List[Any]          =							None  # important for when deepspeed_init is used as re-init
        UpperCamelCase				:   Tuple          =							list(filter(lambda snake_case_    : p.requires_grad						,model.parameters()    )    )
        UpperCamelCase				:   Dict          =							deepspeed_optim_sched(
            snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_    )

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

    return optimizer, lr_scheduler


def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Optional[Any]    ):







    '''simple docstring'''
    import glob

    UpperCamelCase				:   List[Any]          =							sorted(glob.glob(f'{checkpoint_path}/global_step*'    )    )

    if len(snake_case_    ) > 0:
        logger.info(f'Attempting to resume from {checkpoint_path}'    )
        # this magically updates self.optimizer and self.lr_scheduler
        UpperCamelCase				:   List[Any]          =							deepspeed_engine.load_checkpoint(
            snake_case_						,load_optimizer_states=snake_case_						,load_lr_scheduler_states=snake_case_    )
        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"""




import math
import sys
class 							lowerCamelCase		:







   def __init__(							self ):
       UpperCamelCase				:   Tuple          =							0
       UpperCamelCase				:   Optional[int]          =							[]
       UpperCamelCase				:   Optional[int]          =							{}  # To store the pos of node in array







   def       a_							(							self ):
       return self.cur_size == 0







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   List[str]          =							self.left(SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   Optional[int]          =							self.right(SCREAMING_SNAKE_CASE_ )
       if lc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   str          =							lc
       else:
           UpperCamelCase				:   Union[str, Any]          =							idx
       if rc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   Optional[Any]          =							rc
       if smallest != idx:
           self.swap(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
           self.min_heapify(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Inserts a node into the Priority Queue
       UpperCamelCase				:   List[Any]          =							self.cur_size
       self.cur_size += 1
       self.array.append((sys.maxsize, tup[1]) )
       self.decrease_key((sys.maxsize, tup[1])    ,      tup[0] )







   def       a_							(							self ):
       # Removes and returns the min element at top of priority queue
       UpperCamelCase				:   str          =							self.array[0][1]
       UpperCamelCase				:   Optional[int]          =							self.array[self.cur_size - 1]
       self.cur_size -= 1
       self.min_heapify(1 )
       del self.pos[min_node]
       return min_node







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of left child
       return 2 * i + 1







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of right child
       return 2 * i + 2







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of parent
       return math.floor(i / 2 )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # swaps array elements at indices i and j
       # update the pos{}
       UpperCamelCase				:   Dict          =							j
       UpperCamelCase				:   List[str]          =							i
       UpperCamelCase				:   Optional[int]          =							self.array[i]
       UpperCamelCase				:   Union[str, Any]          =							self.array[j]
       UpperCamelCase				:   Optional[int]          =							temp







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Tuple          =							self.pos[tup[1]]
       # assuming the new_d is atmost old_d
       UpperCamelCase				:   Union[str, Any]          =							(new_d, tup[1])
       while idx > 0 and self.array[self.par(SCREAMING_SNAKE_CASE_ )][0] > self.array[idx][0]:
           self.swap(SCREAMING_SNAKE_CASE_    ,      self.par(SCREAMING_SNAKE_CASE_ ) )
           UpperCamelCase				:   Union[str, Any]          =							self.par(SCREAMING_SNAKE_CASE_ )







class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Any          =							{}  # To store graph: u -> (v,w)
       UpperCamelCase				:   int          =							num  # Number of nodes in graph
       # To store the distance from source vertex
       UpperCamelCase				:   Any          =							[0] * self.num_nodes
       UpperCamelCase				:   Union[str, Any]          =							[-1] * self.num_nodes  # To store the path







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       #  Edge going from node u to v and v to u with weight w
       # u (w)-> v, v (w) -> u
       # Check if u already in graph
       if u in self.adjList:
           self.adjList[u].append((v, w) )
       else:
           UpperCamelCase				:   Optional[int]          =							[(v, w)]

       # Assuming undirected graph
       if v in self.adjList:
           self.adjList[v].append((u, w) )
       else:
           UpperCamelCase				:   List[Any]          =							[(u, w)]







   def       a_							(							self ):
       # u -> v(w)
       for u in self.adjList:
           print(SCREAMING_SNAKE_CASE_    ,      """->"""    ,      """ -> """.join(str(f'{v}({w})' ) for v, w in self.adjList[u] ) )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Flush old junk values in par[]
       UpperCamelCase				:   Tuple          =							[-1] * self.num_nodes
       # src is the source node
       UpperCamelCase				:   Dict          =							0
       UpperCamelCase				:   int          =							PriorityQueue()
       q.insert((0, src) )  # (dist from src, node)
       for u in self.adjList:
           if u != src:
               UpperCamelCase				:   Any          =							sys.maxsize  # Infinity
               UpperCamelCase				:   int          =							-1

       while not q.is_empty():
           UpperCamelCase				:   int          =							q.extract_min()  # Returns node with the min dist from source
           # Update the distance of all the neighbours of u and
           # if their prev dist was INFINITY then push them in Q
           for v, w in self.adjList[u]:
               UpperCamelCase				:   List[Any]          =							self.dist[u] + w
               if self.dist[v] > new_dist:
                   if self.dist[v] == sys.maxsize:
                       q.insert((new_dist, v) )
                   else:
                       q.decrease_key((self.dist[v], v)    ,      SCREAMING_SNAKE_CASE_ )
                   UpperCamelCase				:   Any          =							new_dist
                   UpperCamelCase				:   Tuple          =							u

        # Show the shortest distances from src
       self.show_distances(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       print(f'Distance from node: {src}' )
       for u in range(self.num_nodes ):
           print(f'Node {u} has distance: {self.dist[u]}' )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # To show the shortest path from src to dest
       # WARNING: Use it *after* calling dijkstra
       UpperCamelCase				:   Tuple          =							[]
       UpperCamelCase				:   Optional[Any]          =							0
       UpperCamelCase				:   int          =							dest
       # Backtracking from dest to src
       while self.par[temp] != -1:
           path.append(SCREAMING_SNAKE_CASE_ )
           if temp != src:
               for v, w in self.adjList[temp]:
                   if v == self.par[temp]:
                       cost += w
                       break
           UpperCamelCase				:   int          =							self.par[temp]
       path.append(SCREAMING_SNAKE_CASE_ )
       path.reverse()

       print(f'----Path to reach {dest} from {src}----' )
       for u in path:
           print(f'{u}'    ,      end=""" """ )
           if u != dest:
               print("""-> """    ,      end="""""" )

       print("""\nTotal cost of path: """    ,      SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
  __A :		int      							=    Graph(9)
  graph.add_edge(0, 1, 4)
  graph.add_edge(0, 7, 8)
  graph.add_edge(1, 2, 8)
  graph.add_edge(1, 7, 11)
  graph.add_edge(2, 3, 7)
  graph.add_edge(2, 8, 2)
  graph.add_edge(2, 5, 4)
  graph.add_edge(3, 4, 9)
  graph.add_edge(3, 5, 14)
  graph.add_edge(4, 5, 10)
  graph.add_edge(5, 6, 2)
  graph.add_edge(6, 7, 1)
  graph.add_edge(6, 8, 6)
  graph.add_edge(7, 8, 7)
  graph.show_graph()
  graph.dijkstra(0)
  graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




