





import math
import sys

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					0
      __lowerCAmelCase								=					[]
      __lowerCAmelCase								=					{}  # To store the pos of node in array





    def 						A		( self    :		str				):

      """simple docstring"""





      return self.cur_size == 0





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase								=					self.left(__UpperCamelCase				)
      __lowerCAmelCase								=					self.right(__UpperCamelCase				)
      if lc < self.cur_size and self.array(__UpperCamelCase				)[0] < self.array(__UpperCamelCase				)[0]:
        __lowerCAmelCase								=					lc
      else:
        __lowerCAmelCase								=					idx
      if rc < self.cur_size and self.array(__UpperCamelCase				)[0] < self.array(__UpperCamelCase				)[0]:
        __lowerCAmelCase								=					rc
      if smallest != idx:
        self.swap(__UpperCamelCase      ,    __UpperCamelCase				)
        self.min_heapify(__UpperCamelCase				)





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					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    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					self.array[0][1]
      __lowerCAmelCase								=					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    :		List[str]      ,    snake_case    :		int				):

      """simple docstring"""





      return 2 * i + 1





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		str				):

      """simple docstring"""





      return 2 * i + 2





    def 						A		( self    :		str      ,    snake_case    :		Dict				):

      """simple docstring"""





      return math.floor(i / 2				)





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		List[Any]      ,    snake_case    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase								=					j
      __lowerCAmelCase								=					i
      __lowerCAmelCase								=					self.array[i]
      __lowerCAmelCase								=					self.array[j]
      __lowerCAmelCase								=					temp





    def 						A		( self    :		List[Any]      ,    snake_case    :		Dict      ,    snake_case    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      __lowerCAmelCase								=					(new_d, tup[1])
      while idx > 0 and self.array[self.par(__UpperCamelCase				)][0] > self.array[idx][0]:
        self.swap(__UpperCamelCase      ,    self.par(__UpperCamelCase				)				)
        __lowerCAmelCase								=					self.par(__UpperCamelCase				)


class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		str      ,    snake_case    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					{}  # To store graph: u -> (v,w)
      __lowerCAmelCase								=					num  # Number of nodes in graph
      # To store the distance from source vertex
      __lowerCAmelCase								=					[0] * self.num_nodes
      __lowerCAmelCase								=					[-1] * self.num_nodes  # To store the path





    def 						A		( self    :		List[Any]      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		Dict      ,    snake_case    :		Tuple				):

      """simple docstring"""





      if u in self.adjList:
        self.adjList[u].append((v, w)				)
      else:
        __lowerCAmelCase								=					[(v, w)]

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





    def 						A		( self    :		int				):

      """simple docstring"""





      for u in self.adjList:
        print(__UpperCamelCase      ,    '''->'''      ,    ''' -> '''.join(str(f'''{v}({w})'''				) for v, w in self.adjList[u]				)				)





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					[-1] * self.num_nodes
      # src is the source node
      __lowerCAmelCase								=					0
      __lowerCAmelCase								=					PriorityQueue()
      q.insert((0, src)				)  # (dist from src, node)
      for u in self.adjList:
        if u != src:
          __lowerCAmelCase								=					sys.maxsize  # Infinity
          __lowerCAmelCase								=					-1

      while not q.is_empty():
        __lowerCAmelCase								=					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]:
          __lowerCAmelCase								=					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)      ,    __UpperCamelCase				)
            __lowerCAmelCase								=					new_dist
            __lowerCAmelCase								=					u

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





    def 						A		( self    :		str      ,    snake_case    :		List[str]				):

      """simple docstring"""





      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    :		Dict      ,    snake_case    :		Optional[Any]      ,    snake_case    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					[]
      __lowerCAmelCase								=					0
      __lowerCAmelCase								=					dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
        path.append(__UpperCamelCase				)
        if temp != src:
          for v, w in self.adjList[temp]:
            if v == self.par[temp]:
              cost += w
              break
        __lowerCAmelCase								=					self.par[temp]
      path.append(__UpperCamelCase				)
      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: '''      ,    __UpperCamelCase				)


if __name__ == "__main__":
      __lowercase				    =      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


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



