



'''simple docstring'''





import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment

def 			a_	(				) -> Optional[Any]:
  __lowerCamelCase			:			Optional[int]       			=					torch.nn.Linear(2			,4			)
  __lowerCamelCase			:			int       			=					torch.optim.AdamW(model.parameters()			,lr=1.0			)
  __lowerCamelCase			:			Tuple       			=					torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase			,max_lr=0.01			,steps_per_epoch=2			,epochs=1			)
  __lowerCamelCase			:			Tuple       			=					DataLoader(TensorDataset(torch.tensor([1, 2, 3]			)			)			)
  __lowerCamelCase			:			Optional[int]       			=					DataLoader(TensorDataset(torch.tensor([4, 5, 6]			)			)			)

  return model, optimizer, scheduler, train_dl, valid_dl

def 			a_	(				_lowerCAmelCase			) -> Union[str, Any]:
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			Optional[int]       			=					torch.nn.Linear(*tuple(model.weight.T.shape			)			).state_dict()
  model.load_state_dict(_UpperCamelCase			)






class    lowerCamelCase_       (							__snake_case	):
  """simple docstring"""




  @require_cuda
  def 		_lowercase	(					self :      Tuple  )   ->   List[Any]:
    __lowerCamelCase			:			List[Any]       			=					Accelerator()
    assert PartialState._shared_state["_cpu"] is False
    assert PartialState._shared_state["device"].type == "cuda"
    with self.assertRaises(_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase			:			Optional[Any]       			=					Accelerator(cpu=_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      Tuple  )   ->   List[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					Accelerator()
    __lowerCamelCase			:			Tuple       			=					GradientState()
    assert state.num_steps == 1
    __lowerCamelCase			:			str       			=					4
    assert state.num_steps == 4

    assert state.sync_gradients is True
    __lowerCamelCase			:			Union[str, Any]       			=					False
    assert state.sync_gradients is False
    GradientState._reset_state()




  def 		_lowercase	(					self :      int  )   ->   int:
    __lowerCamelCase			:			str       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Optional[Any]       			=					create_components()

    (
        (
        __lowerCamelCase  
    )			,(
        __lowerCamelCase  
    )			,(
        __lowerCamelCase  
    )			,(
        __lowerCamelCase  
    )			,(
        __lowerCamelCase  
    )			,  
    )			:			Optional[Any]       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )

    self.assertTrue(prepared_model in accelerator._models  )
    self.assertTrue(prepared_optimizer in accelerator._optimizers  )
    self.assertTrue(prepared_scheduler in accelerator._schedulers  )
    self.assertTrue(prepared_train_dl in accelerator._dataloaders  )
    self.assertTrue(prepared_valid_dl in accelerator._dataloaders  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   int:
    __lowerCamelCase			:			str       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					create_components()
    accelerator.prepare(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )
    accelerator.free_memory()

    self.assertTrue(len(accelerator._models  ) == 0  )
    self.assertTrue(len(accelerator._optimizers  ) == 0  )
    self.assertTrue(len(accelerator._schedulers  ) == 0  )
    self.assertTrue(len(accelerator._dataloaders  ) == 0  )




  def 		_lowercase	(					self :      Any  )   ->   Union[str, Any]:
    PartialState._reset_state()

    # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
    def noop(*_a :      Optional[int]     ,      **_a :      str  ):
      pass

    with patch('torch.cuda.set_device'     ,      _SCREAMING_SNAKE_CASE  ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'  ):
      __lowerCamelCase			:			Dict       			=					Accelerator()
      self.assertEqual(str(accelerator.state.device  )     ,      'cuda:64'  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    __lowerCamelCase			:			Union[str, Any]       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					create_components()
    accelerator.prepare(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )

    __lowerCamelCase			:			str       			=					get_signature(_SCREAMING_SNAKE_CASE  )

    with tempfile.TemporaryDirectory() as tmpdirname:
      accelerator.save_state(_SCREAMING_SNAKE_CASE  )

      # make sure random weights don't match
      load_random_weights(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) > 1e-3  )

      # make sure loaded weights match
      accelerator.load_state(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) < 1e-3  )




  def 		_lowercase	(					self :      Any  )   ->   List[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					create_components()
    accelerator.prepare(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )

    __lowerCamelCase			:			Union[str, Any]       			=					get_signature(_SCREAMING_SNAKE_CASE  )

    # saving hook
    def save_config(_a :      List[Any]     ,      _a :      Optional[Any]     ,      _a :      List[Any]  ):
      __lowerCamelCase			:			Any       			=					{'class_name': models[0].__class__.__name__}

      with open(os.path.join(_SCREAMING_SNAKE_CASE     ,      'data.json'  )     ,      'w'  ) as f:
        json.dump(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )

        # loading hook
    def load_config(_a :      Dict     ,      _a :      Optional[Any]  ):
      with open(os.path.join(_SCREAMING_SNAKE_CASE     ,      'data.json'  )     ,      'r'  ) as f:
        __lowerCamelCase			:			str       			=					json.load(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase			:			List[Any]       			=					config['class_name']

    __lowerCamelCase			:			int       			=					accelerator.register_save_state_pre_hook(_SCREAMING_SNAKE_CASE  )
    __lowerCamelCase			:			Dict       			=					accelerator.register_load_state_pre_hook(_SCREAMING_SNAKE_CASE  )

    with tempfile.TemporaryDirectory() as tmpdirname:
      accelerator.save_state(_SCREAMING_SNAKE_CASE  )

      # make sure random weights don't match with hooks
      load_random_weights(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) > 1e-3  )

      # random class name to verify correct one is loaded
      __lowerCamelCase			:			Optional[Any]       			=					'random'

      # make sure loaded weights match with hooks
      accelerator.load_state(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) < 1e-3  )

      # mode.class_name is loaded from config
      self.assertTrue(model.class_name == model.__class__.__name__  )

    # remove hooks
    save_hook.remove()
    load_hook.remove()

    with tempfile.TemporaryDirectory() as tmpdirname:
      accelerator.save_state(_SCREAMING_SNAKE_CASE  )

      # make sure random weights don't match with hooks removed
      load_random_weights(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) > 1e-3  )

      # random class name to verify correct one is loaded
      __lowerCamelCase			:			Optional[Any]       			=					'random'

      # make sure loaded weights match with hooks removed
      accelerator.load_state(_SCREAMING_SNAKE_CASE  )
      self.assertTrue(abs(model_signature - get_signature(_SCREAMING_SNAKE_CASE  )  ) < 1e-3  )

      # mode.class_name is NOT loaded from config
      self.assertTrue(model.class_name != model.__class__.__name__  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Optional[int]:
    __lowerCamelCase			:			Union[str, Any]       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					create_components()
    __lowerCamelCase			:			str       			=					None

    # This should work
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					accelerator.prepare(
        _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )
    self.assertTrue(dummy_obj is None  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					Accelerator()
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					create_components()
    __lowerCamelCase			:			Dict       			=					[1, 2, 3]

    # This should work
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					accelerator.prepare(
        _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Dummy object should have `_is_accelerate_prepared` set to `True`'     ,      )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Model is missing `_is_accelerator_prepared` or is set to `False`'     ,      )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'     ,      )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'     ,      )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'     ,      )
    self.assertEqual(
        getattr(_SCREAMING_SNAKE_CASE     ,      '_is_accelerate_prepared'     ,      _SCREAMING_SNAKE_CASE  )     ,      _SCREAMING_SNAKE_CASE     ,      'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'     ,      )




  @slow
  @require_bnb
  def 		_lowercase	(					self :      Tuple  )   ->   Any:
    from transformers import AutoModelForCausalLM

    __lowerCamelCase			:			int       			=					AutoModelForCausalLM.from_pretrained(
        'EleutherAI/gpt-neo-125m'     ,      load_in_abit=_SCREAMING_SNAKE_CASE     ,      device_map={'': 0}     ,      )
    __lowerCamelCase			:			Optional[Any]       			=					Accelerator()

    # This should work
    __lowerCamelCase			:			List[Any]       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )




  @slow
  @require_bnb
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    from transformers import AutoModelForCausalLM

    __lowerCamelCase			:			Tuple       			=					Accelerator()

    with init_empty_weights():
      __lowerCamelCase			:			int       			=					AutoModelForCausalLM.from_pretrained(
          'EleutherAI/gpt-neo-125m'     ,      )
      model.tie_weights()
      __lowerCamelCase			:			Tuple       			=					infer_auto_device_map(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase			:			List[str]       			=					'cpu'

    __lowerCamelCase			:			Dict       			=					AutoModelForCausalLM.from_pretrained(
        'EleutherAI/gpt-neo-125m'     ,      device_map=_SCREAMING_SNAKE_CASE     ,      load_in_abit=_SCREAMING_SNAKE_CASE     ,      llm_inta_enable_fpaa_cpu_offload=_SCREAMING_SNAKE_CASE  )

    # This should not work and get value error
    with self.assertRaises(_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase			:			List[Any]       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )




  @slow
  @require_bnb
  @require_multi_gpu
  def 		_lowercase	(					self :      int  )   ->   Dict:
    from transformers import AutoModelForCausalLM

    __lowerCamelCase			:			Union[str, Any]       			=					{'distributed_type': DistributedType.MULTI_GPU}

    with init_empty_weights():
      __lowerCamelCase			:			List[str]       			=					AutoModelForCausalLM.from_pretrained(
          'EleutherAI/gpt-neo-125m'     ,      )
      model.tie_weights()
      __lowerCamelCase			:			Optional[Any]       			=					infer_auto_device_map(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase			:			Any       			=					1

    __lowerCamelCase			:			Union[str, Any]       			=					AutoModelForCausalLM.from_pretrained(
        'EleutherAI/gpt-neo-125m'     ,      load_in_abit=_SCREAMING_SNAKE_CASE     ,      device_map=_SCREAMING_SNAKE_CASE     ,      )
    __lowerCamelCase			:			List[Any]       			=					Accelerator()

    # This should not work and get value error
    with self.assertRaises(_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase			:			List[Any]       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )

    PartialState._reset_state()




  @slow
  @require_bnb
  @require_multi_gpu
  def 		_lowercase	(					self :      int  )   ->   int:
    from transformers import AutoModelForCausalLM

    with init_empty_weights():
      __lowerCamelCase			:			List[str]       			=					AutoModelForCausalLM.from_pretrained(
          'EleutherAI/gpt-neo-125m'     ,      )
      __lowerCamelCase			:			int       			=					infer_auto_device_map(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase			:			Optional[int]       			=					1

    __lowerCamelCase			:			Dict       			=					AutoModelForCausalLM.from_pretrained(
        'EleutherAI/gpt-neo-125m'     ,      load_in_abit=_SCREAMING_SNAKE_CASE     ,      device_map=_SCREAMING_SNAKE_CASE     ,      )
    __lowerCamelCase			:			Union[str, Any]       			=					Accelerator()

    # This should work
    __lowerCamelCase			:			Tuple       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )




  @require_cuda
  def 		_lowercase	(					self :      str  )   ->   Any:
    __lowerCamelCase			:			List[str]       			=					torch.nn.Linear(10     ,      10  )
    __lowerCamelCase			:			int       			=					torch.optim.SGD(model.parameters()     ,      lr=0.01  )
    __lowerCamelCase			:			List[Any]       			=					Accelerator(cpu=_SCREAMING_SNAKE_CASE  )
    __lowerCamelCase			:			Tuple       			=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )














class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str = ""   ,						_SCREAMING_SNAKE_CASE      :    bool = False ):






           """simple docstring"""




           UpperCamelCase					       =  {}

           # A node will be a leaf if the tree contains its word
           UpperCamelCase					       =  is_leaf

           UpperCamelCase					       =  prefix







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for q, w in zip(self.prefix   ,						_SCREAMING_SNAKE_CASE ):
                 if q != w:
                       break

                 x += 1

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    list[str] ):






           """simple docstring"""




           for word in words:
                 self.insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if self.prefix == word:
                 UpperCamelCase					       =  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:
                 UpperCamelCase					       =  RadixNode(prefix=_SCREAMING_SNAKE_CASE   ,						is_leaf=_SCREAMING_SNAKE_CASE )

           else:
                 UpperCamelCase					       =  self.nodes[word[0]]
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )

                 # 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(_SCREAMING_SNAKE_CASE )

                 # 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:
                       UpperCamelCase					       =  remaining_prefix

                       UpperCamelCase					       =  self.nodes[matching_string[0]]
                       UpperCamelCase					       =  RadixNode(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  aux_node

                       if remaining_word == "":
                             UpperCamelCase					       =  True
                       else:
                             self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # 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(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # 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(_SCREAMING_SNAKE_CASE )
                 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:
                                         UpperCamelCase					       =  list(self.nodes.values() )[0]
                                         UpperCamelCase					       =  merging_node.is_leaf
                                         self.prefix += merging_node.prefix
                                         UpperCamelCase					       =  merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                             elif len(incoming_node.nodes ) > 1:
                                   UpperCamelCase					       =  False
                             # If there is 1 edge, we merge it with its child
                             else:
                                   UpperCamelCase					       =  list(incoming_node.nodes.values() )[0]
                                   UpperCamelCase					       =  merging_node.is_leaf
                                   incoming_node.prefix += merging_node.prefix
                                   UpperCamelCase					       =  merging_node.nodes

                             return True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    int = 0 ):






           """simple docstring"""




           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 		lowercase__  (				) ->			bool:
      """simple docstring"""

      UpperCamelCase					       =  'banana bananas bandana band apple all beast'.split()
      UpperCamelCase					       =  RadixNode()
      root.insert_many(_UpperCamelCase)

      assert all(root.find(_UpperCamelCase) 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 		lowercase__  (				) ->			None:
      """simple docstring"""

      assert test_trie()
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      UpperCamelCase					       =  RadixNode()
      UpperCamelCase					       =  'banana bananas bandanas bandana band apple all beast'.split()
      root.insert_many(_UpperCamelCase)

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


if __name__ == "__main__":
       main()



