

class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a = ""						,   a = False):
         # Mapping from the first character of the prefix of the node
         lowercase__    :   dict[str, RadixNode]      =       {}

         # A node will be a leaf if the tree contains its word
         lowercase__    :   Optional[Any]      =       is_leaf

         lowercase__    :   Optional[Any]      =       prefix





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[str]      =       0
         for q, w in zip(self.prefix						,   a):
              if q != w:
                   break

              x += 1

         return self.prefix[:x], self.prefix[x:], word[x:]





    def       snake_case_				(      self						,   a):
         for word in words:
              self.insert(a)





    def       snake_case_				(      self						,   a):
         # Case 1: If the word is the prefix of the node
         # Solution: We set the current node as leaf
         if self.prefix == word:
              lowercase__    :   str      =       True

         # Case 2: The node has no edges that have a prefix to the word
         # Solution: We create an edge from the current node to a new one
         # containing the word
         elif word[0] not in self.nodes:
              lowercase__    :   str      =       RadixNode(prefix=a						,   is_leaf=a)

         else:
              lowercase__    :   Optional[Any]      =       self.nodes[word[0]]
              lowercase__      , lowercase__      , lowercase__    :   Optional[int]      =       incoming_node.match(
                  a)

              # Case 3: The node prefix is equal to the matching
              # Solution: We insert remaining word on the next node
              if remaining_prefix == "":
                   self.nodes[matching_string[0]].insert(a)

              # Case 4: The word is greater equal to the matching
              # Solution: Create a node in between both nodes, change
              # prefixes and add the new node for the remaining word
              else:
                   lowercase__    :   Dict      =       remaining_prefix

                   lowercase__    :   Optional[int]      =       self.nodes[matching_string[0]]
                   lowercase__    :   List[str]      =       RadixNode(a						,   a)
                   lowercase__    :   int      =       aux_node

                   if remaining_word == "":
                        lowercase__    :   Dict      =       True
                   else:
                        self.nodes[matching_string[0]].insert(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Tuple      =       self.nodes.get(word[0]						,   a)
         if not incoming_node:
              return False
         else:
              lowercase__      , lowercase__      , lowercase__    :   Union[str, Any]      =       incoming_node.match(
                  a)
              # If there is remaining prefix, the word can't be on the tree
              if remaining_prefix != "":
                   return False
              # This applies when the word and the prefix are equal
              elif remaining_word == "":
                   return incoming_node.is_leaf
              # We have word remaining so we check the next node
              else:
                   return incoming_node.find(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       self.nodes.get(word[0]						,   a)
         if not incoming_node:
              return False
         else:
              lowercase__      , lowercase__      , lowercase__    :   int      =       incoming_node.match(
                  a)
              # If there is remaining prefix, the word can't be on the tree
              if remaining_prefix != "":
                   return False
              # We have word remaining so we check the next node
              elif remaining_word != "":
                   return incoming_node.delete(a)
              else:
                   # If it is not a leaf, we don't have to delete
                   if not incoming_node.is_leaf:
                        return False
                   else:
                        # We delete the nodes if no edges go from it
                        if len(incoming_node.nodes) == 0:
                             del self.nodes[word[0]]
                             # We merge the current node with its only child
                             if len(self.nodes) == 1 and not self.is_leaf:
                                  lowercase__    :   List[Any]      =       list(self.nodes.values())[0]
                                  lowercase__    :   str      =       merging_node.is_leaf
                                  self.prefix += merging_node.prefix
                                  lowercase__    :   Optional[int]      =       merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                        elif len(incoming_node.nodes) > 1:
                             lowercase__    :   Dict      =       False
                        # If there is 1 edge, we merge it with its child
                        else:
                             lowercase__    :   Tuple      =       list(incoming_node.nodes.values())[0]
                             lowercase__    :   Any      =       merging_node.is_leaf
                             incoming_node.prefix += merging_node.prefix
                             lowercase__    :   List[str]      =       merging_node.nodes

                        return True





    def       snake_case_				(      self						,   a = 0):
         if self.prefix != "":
              print('-' * height						,   self.prefix						,   '  (leaf)' if self.is_leaf else '')

         for value in self.nodes.values():
              value.print_tree(height + 1)



def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       'banana bananas bandana band apple all beast'.split()
     lowercase__    :   Dict      =       RadixNode()
     root.insert_many(SCREAMING_SNAKE_CASE_			)

     assert all(root.find(SCREAMING_SNAKE_CASE_			) for word in words			)
     assert not root.find('bandanas'			)
     assert not root.find('apps'			)
     root.delete('all'			)
     assert not root.find('all'			)
     root.delete('banana'			)
     assert not root.find('banana'			)
     assert root.find('bananas'			)

     return True

def 	snake_case__			(    ):



     '''simple docstring'''





     assert test_trie()

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   int      =       RadixNode()
     lowercase__    :   List[str]      =       'banana bananas bandanas bandana band apple all beast'.split()
     root.insert_many(SCREAMING_SNAKE_CASE_			)

     print('Words:'	,     SCREAMING_SNAKE_CASE_			)
     print('Tree:'			)
     root.print_tree()


if __name__ == "__main__":
       main()




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

